Spencer shows compelling evidence of UHI in CRUTem3 data

Above graph showing UHI by county population in California, from Goodridge 1996, published in the Bulletin of the American Meteorological Society.

McKitrick & Michaels Were Right: More Evidence of Spurious Warming in the IPCC Surface Temperature Dataset

Guest post by Roy W. Spencer, Ph. D.

The supposed gold standard in surface temperature data is that produced by Univ. of East Anglia, the so-called CRUTem3 dataset. There has always been a lingering suspicion among skeptics that some portion of this IPCC official temperature record contains some level of residual spurious warming due to the urban heat island effect. Several published papers over the years have supported that suspicion.

The Urban Heat Island (UHI) effect is familiar to most people: towns and cities are typically warmer than surrounding rural areas due to the replacement of natural vegetation with manmade structures. If that effect increases over time at thermometer sites, there will be a spurious warming component to regional or global temperature trends computed from the data.

Here I will show based upon unadjusted International Surface Hourly (ISH) data archived at NCDC that the warming trend over the Northern Hemisphere, where virtually all of the thermometer data exist, is a function of population density at the thermometer site.

Depending upon how low in population density one extends the results, the level of spurious warming in the CRUTem3 dataset ranges from 14% to 30% when 3 population density classes are considered, and even 60% with 5 population classes.

DATA & METHOD

Analysis of the raw station data is not for the faint of heart. For the period 1973 through 2011, there are hundreds of thousands of data files in the NCDC ISH archive, each file representing one station of data from one year. The data volume is many gigabytes.

From these files I computed daily average temperatures at each station which had records extending back at least to 1973, the year of a large increase in the number of global stations included in the ISH database. The daily average temperature was computed from the 4 standard synoptic times (00, 06, 12, 18 UTC) which are the most commonly reported times from stations around the world.

At least 20 days of complete data were required for a monthly average temperature to be computed, and the 1973-2011 period of record had to be at least 80% complete for a station to be included in the analysis.

I then stratified the stations based upon the 2000 census population density at each station; the population dataset I used has a spatial resolution of 1 km.

I then accepted all 5×5 deg lat/lon grid boxes (the same ones that Phil Jones uses in constructing the CRUTem3 dataset) which had all of the following present: a CRUTem3 temperature, and at least 1 station from each of 3 population classes, with class boundaries at 0, 15, 500, and 30,000 persons per sq. km.

By requiring all three population classes to be present for grids to be used in the analysis, we get the best ‘apples-to-apples’ comparison between stations of different population densities. The downside is that there is less geographic coverage than that provided in the Jones dataset, since relatively few grids meet such a requirement.

But the intent here is not to get a best estimate of temperature trends for the 1973-2011 period; it is instead to get an estimate of the level of spurious warming in the CRUTem3 dataset. The resulting number of 5×5 deg grids with stations from all three population classes averaged around 100 per month during 1973 through 2011.

RESULTS

The results are shown in the following figure, which indicates that the lower the population density surrounding a temperature station, the lower the average linear warming trend for the 1973-2011 period. Note that the CRUTem3 trend is a little higher than simply averaging all of the accepted ISH stations together, but not as high as when only the highest population stations were used.

The CRUTem3 and lowest population density temperature anomaly time series which go into computing these trends are shown in the next plot, along with polynomial fits to the data:

Again, the above plot is not meant to necessarily be estimates for the entire Northern Hemispheric land area, but only those 5×5 deg grids where there are temperature reporting stations representing all three population classes.

The difference between these two temperature traces is shown next:

From this last plot, we see in recent years there appears to be a growing bias in the CRUTem3 temperatures versus the temperatures from the lowest population class.

The CRUTem3 temperature linear trend is about 15% warmer than the lowest population class temperature trend. But if we extrapolate the results in the first plot above to near-zero population density (0.1 persons per sq. km), we get a 30% overestimate of temperature trends from CRUTem3.

If I increase the number of population classes from 3 to 5, the CRUTem3 trend is overestimated by 60% at 0.1 persons per sq. km, but the number of grids which have stations representing all 5 population classes averages only 10 to 15 per month, instead of 100 per month. So, I suspect those results are less reliable.

I find the above results to be quite compelling evidence for what Anthony Watts, Pat Michaels, Ross McKitrick, et al., have been emphasizing for years: that poor thermometer siting has likely led to spurious warming trends, which has then inflated the official IPCC estimates of warming. These results are roughly consistent with the McKitrick and Michaels (2007) study which suggested as much as 50% of the reported surface warming since 1980 could be spurious.

I would love to write this work up and submit it for publication, but I am growing weary of the IPCC gatekeepers killing my papers; the more damaging any conclusions are to the IPCC narrative, the less likely they are to be published. That’s the world we live in.

UPDATE: I’ve appended the results for the U.S. only, which shows evidence that CRUTem3 has overstated U.S. warming trends during 1973-2011 by at least 50%.

I’ve computed results for just the United States, and these are a little more specific. The ISH stations were once again stratified by local population density. Temperature trends were computed for each station individually, and the upper and lower 5% trend ‘outliers’ in each of the 3 population classes were excluded from the analysis. For each population class, I also computed the ‘official’ CRUTem3 trends, and averaged those just like I averaged the ISH station data.

The results in the following plot show that for the 87 stations in the lowest population class, the average CRUTem3 temperature trend was 57% warmer than the trend computed from the ISH station data.
These are apples-to-apples comparisons…for each station trend included in the averaging for each population class, a corresponding, nearest-neighbor CRUTem3 trend was also included in the averaging for that population class.

How can one explain such results, other than to conclude that there is spurious warming in the CRUTem3 dataset? I already see in the comments, below, that there are a few attempts to divert attention from this central issue. I would like to hear an alternative explanation for such results.

Problem is that he only uses NH sites so the bias estimate has a spatial error.
UHI varies with Latitude.
It’s higher in the NH than in the SH.
That said, Roy’s results are pretty close to the results we showed at AGU. which was .04C
per decade from 1979 to 2011.

extrapolating down to lower populations is suspect because of the heavy modelling used to derive
the 1km resolution population density data. To understand that you have to read the papers behind the GRUMPV1 population density data.

[SNIP – Mr. Barney, take this sort of ranting about women, religious issues, etc elsewhere. There is no place for this sort of ugliness here. You’ve been snipped several times before. Final warning. – Anthony Watts]

I would love to write this work up and submit it for publication, but I am growing weary of the IPCC gatekeepers killing my papers; the more damaging any conclusions are to the IPCC narrative, the less likely they are to be published. That’s the world we live in.

then set up another avenue of peer review, open to everyone, with a press release of why.

I tried including SH sites, Steve, but there were none that met the inclusion criteria. Also, see the update to my post…even without extrapolation, the results over the U.S. show a 57% (!) warmer CRUTem3 trend versus the low population station data.
-Roy

This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

The study is very valuable. However, it does show differences in trends between places with different levels of population density in 2000, but it does not show that increasing population density correlates with increasing warming over time. That would require having population data not only for year 2000, but also for other years (at least for 1970, near the beginning of the series of temps studied). Passing from cross-section data to longitudinal analysis may be tricky.

Besides, residential population density is not the whole story about UHI. Some downtown areas have little residential population but high density of offices and other structures capturing and generating heat, and also intense vehicle circulation. Industrial areas are similar in this regard, with little population living there but lots of factories, refineries, machinery and trucks going around. Also, from the survey of US stations led by Anthony some time ago we learned that many isolated rural stations are now on top of a tin or concrete roof, or in a concrete or asphalt parking lot, but were probably in greener surroundings in the past.

What should be shown is that an increasing trend in pop density and/or other relevant features (buildings, vehicles, engines, generators, highways, airports and the like) correlates with a higher trend in temperature.

However, this analysis is a very useful addition to the literature on UHI.

This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

Maybe it’s all that additional CO2 they find in the homes of MSA’s–isn’t that what keeps everything warm, that CO2?

Based on your statement, however, the answer to your question is obvious.

So if 50% is from UHI, and up to 50% is from natural cycles (LIA recovery), then the gold standard temperature record will never actually decline, but only level off if “true” temperatures are in fact in long term natural decline. As for CO2, well, never mind.

Mr Pepper,
If the majority of temperature stations world wide are in urban areas then the fact that urban areas are only 0.5% of the surface is meaningless. The temperatures used to plot an average worldwide temperature is coming from predominately urban areas. So actually BEST didn’t really deal with the issue at hand.

Hugh Pepper says “This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?”

A classic goofy, alarmist half truth: Obviously only a tiny part of the world is urbanised, but equally obviously most of the world’s temperature monitoring stations are located in that tiny part. These stations then bias the results from the other circa 99.5% of the world.

Would it be worthwhile to focus on some long term stations that meet the Cat 1 or 2 standard and have experienced transition from rural to urban if they can be linked to some nearby long term Cat 1 or 2 station that has stayed rural? Would suggest that the light density photos from satellite would be the best way to define urban/rural, as it is more infrastructure specific than population.

Would seem this approach would get away from models and be strictly observation driven.

Hugh Pepper says:
March 30, 2012 at 12:46 pm
This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

That is faulty logic, IMHO.
If only 0.5% of the world is urbanized, then only 0.5% of the weather stations used to calculate global temps should be in urbanized areas. It is more logical, IMHO.

This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

BEST were part of the “avoid the real question and pretend to answer it with another one”.

Yes UHI contributes negligibly to global temperatures.

But what is of concern is how many of the temperature readings are contaminated by UHI.

If we were trying to get an actual “global temperature”, whatever that might mean, we would not want to use a data based known to be heavily contaminated. Unless, of course, we want the answer to be heavily contaminated.

I know that it goes without saying, but do note that Ross and I were only (obviously) looking at land data, and adjusting for the percent of land in each hemisphere gave us a reduction in total global warming of about 18%. That 50% figure that people often quote is within the land data only. Interestingly, our 2007 result changed the distribution of warming to look very much like the distribution of the satellite warming records, cutting off the extreme tails in the thermometric record.

Also, correcting the land record for our nonthermometric effect gave us the same rate of warming as in the Spencer and Christy MSU.

Roy–remember that the Climategaters went after deFreitas for publishing our paper. I have since found out that the situation is now worse, having manuscripts just being rejected out of hand that clearly merit at least a review.

Thanks for this article and the professionalism of the site. I would be very interested in hearing a comment from the author or another expert in response to Hugh Pepper’s point about the BEST study. Is David Schofield’s suggestion correct? It seems distribution of measuring locations would be taken into account by any well constructed study.

As David Schofield says at 12:52pm the TV, and radio, forecasts always note that the temperatures will be higher in the cities. Especially for overnight lows. UHI is assumed. Built into the forecast.

It also occurs to do that UHI is not only increasing due to growth and sprawl but on a per capita basis we’re also increasing our energy usage so UHI is probably increasing even where population levels are static.

Hugh Pepper says:
March 30, 2012 at 12:46 pm
This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average.

If that is their conclusion then they are idiots. What matters is the percentage of the THERMOMETERS that are urbanized. (David Schofield beat me to it).

BTW, in the BEST study 1/3 of the sites showed cooling, and those were mixed well among the sites that showed warming, so the differences were not due to regional effects but are what one would expect with UHI and micro-climate warming.

Well done sirs.. Now we have what looks like reasonably solid evidence of UHI affecting the calculated global land temp.

Then you think of the loss of all those remote stations which would be in non-dense population areas thus placing more emphasis on those in denser populations, and the constant “adjustments” to make the past colder, and you really have to wonder if there has actually been much warming at all !!

Certainly the records and calculations done by the AGW priesthood (Hansen, Jones, CRU) are NOT going to give us a reliable answer. !!

Lets accept, Had CRUT3 is boosted upward by UHI to some degree, and
HadCRUT4 is boosted further by warm spot chasing in the Arctic…..
Plain sneakiness in statistics, we know this type of people…..
But…..this cover-up of the temp decline is futile, because we have reached
the top temp plateau already from which it cannot get any warmer…. they
achieve only to buy time for a couple more years until the full truth of temp
decline will globally be evident…..
JS

“A classic goofy, alarmist half truth: Obviously only a tiny part of the world is urbanised, but equally obviously most of the world’s temperature monitoring stations are located in that tiny part. These stations then bias the results from the other circa 99.5% of the world.”

Because UHI warming depends on an increase over time, what your study here has shown is that the UHI warming increases faster in higher population density areas. Doesn’t this disagree with your previous results?

Hansen and Jones have dismissed the UHIE influence on the global temp profile by saying that the seriously urban part of the record is a small part of the record. However, if in their adjustments they adjust rural sites to reflect urban sites, and not the other way around, then a greater part of the record is compromised.

The main thing is, when the urban centres only are considered, there should be a REDUCTION to the adjustment effect. If isolation of serious urban data does not show the reduction, then there has been no net UHIE adjustments. Then the effect is both real and, for normalization purposes, pushed into the general dataset.

“I tried including SH sites, Steve, but there were none that met the inclusion criteria. Also, see the update to my post…even without extrapolation, the results over the U.S. show a 57% (!) warmer CRUTem3 trend versus the low population station data.
-Roy”

Yes, the SH stations in ISH and GHCN daily are rather sparse. So, you need to add in other data sources IF you want to understand the UHI bias in the complete record. If I wanted to show the highest bias possible I would just pick a NH dataset. That’s well known.

What you should find ( consistent with UHI literature) is that UHI has a statistically significant relationship to latitude. That is, UHI is lower in the SH than it is in the NH. If you want to establish a bias in the whole record ( spatially complete) then you have to find data in the SH.
Further, I’m not sure you weighted your data the same way CRU weights its data per grid cell.
That’s a minor detail but important. Its also unclear if you looked at CRU grids on a monthly basis.

Further, I would not use GRUMPv1 to do an analysis of the US. There are much better datasets, especially for the US. basically with GRUMPv1 your 1km data is the result of a model.
Again, one of the things you should do is calibrate GRUMPv1 against some known quantities.
That’s pretty easy. If the producers of GRUMPv1 had done a producers accuracy test you could
just cite that, but I havent seen one. Basically, you need to audit GRUMPv1 before just using it.

The fact of papers being automatically rejected is the worst thing I have read here today. We knew they applied pressure, but the idea that the publishers have now lost all sense of propriety means basically science is dead within their pages. If these blogs can’t make up the difference in at least keeping the ideas out there, then we are headed for a darker age then after the fall of Rome.

Suggestions: I know you are doing apples to apples, but 5×5 degree gridding is absurd as it builds in a horrendous projective correction near the poles, and one that is reasonably accurate only near the equator. I’d strongly suggest building an icosahedral tiling of the sphere at a scale-adustable granularity. That way you can scale down to tile sizes that permit fine-grained assignment of stations and populations, and can apply simple affinity/range rules for building the local estimates of UHI warming, without having to worry about whether the grids are in CA, Maine, Alaska, or Ecuador (all of which have very different areas in a 5×5 grid cell no matter how you try to correct for that areal difference. The problem is that the gridding over the sphere is not uniform in the first place, and just accounting for the spherical jacobean cannot “fix” this in the statistics (at least not without a lot of expertise that I strongly suspect is not being correctly applied). A uniform tiling lets you do straight-up statistics with no fanciness.

Aside from the increased ease in doing the stats and integrals right with a good tiling (which can be made entirely automatic in computer code, right, so it is only “difficult” once and then is easy forever after) I really suspect that looking at the actual structure of the data on moderately fine grained icosahedral grid would provide a rather big hit of pure insight, especially if you are seeking to uncover either deliberate if occult bias (of the sort that makes all “adjustments” make the past cooler) or Anthony’s hypothesized poor-siting kind of bias.

As for publishing it, an icosahedral gridding might well help. This is something that might interest a reviewer enough to publish your paper for the method, even if they (want to) disagree with your conclusion. For one thing, it permits lots of folks to save face (if nothing else) if a scalable icosahedral tiling reveals problems or general structures that weren’t seen with lat/lon tiles. It “explains” why they were wrong without making them deliberately wrong, as it were. Indeed, you might well consider calling the paper “Comparing of icosahedral versus lat/lon tiling strategies in Climate Science” and only incidentally point out the resulting 57% underestimation of the UHI effect in at least some of CRUTem4.

It actually paves the way for taking the same data and doing a much better straight up estimate of global temperature, in a defensible way, and one that ALSO badly needs to be applied to the incoming ARGO data.

Hugh Pepper says:
March 30, 2012 at 12:46 pm
This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

That was quite specious logic when proposed by BEST as it has nothing to do with where the temperature records were recorded. Are 99.5% of the temperature records used in BEST from non-urbanized sites?

This was completely debunked months ago. I’m surprised anybody is still trying to get by with repeating it.

“At least 20 days of complete data were required for a monthly average temperature to be computed, and the 1973-2011 period of record had to be at least 80% complete for a station to be included in the analysis.”

Why? I’d like to hear more justification for this, because these type of decisions are often the bit which decides the results. Did you try with other figures to see what results you got?

Several comments assert that discrepancies can occur because thermometer readings are inaccurate or are calculated disproportionately. But be aware that the BEST studies were based, not only on land-based instruments, but also on satellite calculations.

” If isolation of serious urban data does not show the reduction, then there has been no net UHIE adjustments. Then the effect is both real and, for normalization purposes, pushed into the general dataset.”

++++++

Well argued. An additional effect is not just the population (which is a proxy for urban development) but the wealth that population comamnds. If one were to use municipal taxation as the proxy, one might find a similar correlation and that the increase in population (noted above to be at a lower rate) is not as good. One might use the reported income of the gridcell population rather than the number of people. UHI is really a development impact, not entirely a population impact.

Hugh Pepper says, March 30, 2012 at 12:46 pm :
“This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?”

But if thermometers used are placed in the “0,5%” of the world with urbanization, then UHI is suddenly dominating.

Ex: From 87 hadcrut stations used by Appinsys.com, the avg population is 1,3 mio people.
A typical Hadcrut (crutem3) station has over 100.000 inhabitants in the US:

Ex, Turkey.
Systematically the 250 stations has been limited in data use so that the rural stations + stations from towns under 50-100.000 inhabitants are limited to years 1960-90. Somewhat larger cities are available from 1950-90 or 1960-2000, and then only the few very largest citites allows us to see the long trends.http://hidethedecline.eu/pages/ruti/asia/turkey.php

Thus, UHI stations are forced to play a much more dominating role.

This and an endless row of other issues appears not to be considered in BEST.
Sceptics should in no way accept BEST. They did not do the needed.

Correct me if I am wrong Dr. Spencer, but my impression was that you were not comparing your dataset to the overall HadCruT trend, but to the trend in their data on a grid square basis. So if you were only comparing the HadCruT trend for that grid to the trend at the stations in that grid, it doesn’t matter if you have SH grids involved or not, right? What you have established is that UHI does in fact affect the calculated trends. A much more detailed analysis would be necessary to discern the degree to which the UHI contamination affects the entire global dataset; but what this analysis has done is significant, because it has shown the UHI contamination to be real and significant.

Face it ladies and gentlemen–surface temperatures are simply data points. They are NOT the actual temperature of the earth (or any significant part of it, for that matter). To arrive at the actual temperature in any given time or place, the data points must be projected to the remaining volume, which is almost infinitely larger than the samples. A highly questionable method called “polynominals” is currently being used to make this jump, but far superior methods exist.

For example, geostatistics is used in mining to project small sample values to much larger volumes and the reason is obvious–the profit motive. When climate science gets to the point where they’re using best available technology for this exercise, then we’ll have a much better estimate. Until then, much of what is discussed regarding this data is just conjecture. Interesting, but still conjecture.

(You might say we’re still at the stage where sample quality is a concern, as this paper indicates–there are serious “contamination” issues with our set of data points.)

“That is, UHI is lower in the SH than it is in the NH. If you want to establish a bias in the whole record ( spatially complete) then you have to find data in the SH.”

Good point. Surely there is sufficient data in Argentina, Chile, South Africa and Australia/NZ to get some idea. If it showed a similar direction of trend, perhaps lesser slope, this would be good support for a global over estimate. I suspect, like the US/Western Europe, you are probably going to find clustering of thermometers in places like N.Z. and Australia but, since that is the best HADCRUT and company can do, it should relate to the global “record”.

Driving through Quartzsite, Arizona, one is impressed by the number of visitors fleeing the brisk weather of Montana, Alberta, North Dakota….. This annual influx of ‘snowbirds’ increases the local population by many thousands every winter.

Worldwide there are any number of other seasonal movements. Oshkosh, Wisconsin, comes to mind. And the Hajj, to Mecca, is probably the mother of all migrations, with an influx of millions.

I have been wondering for some time if it might be possible to detect a periodic temperature signal from these locations that would give a clue to the magnitude of UHIs.

The spatial distribution should be taken into account, especially in terms of assessing UHI – but the only real way is to ‘cut down’ the number of stations used in the total analysis from the known biased areas (i.e. UHI). But I still don’t accept there can ever be a realistic global temp analysis or trend due to all the discrepencies no matter how well it’s bloody modelled!
As far as I can see, only rural, well sited, well documented, top class and top maintained sites can be used for any analysis. All urban sites should be discounted IMO. I cannot see there being many of those rural sites around, but those are the ones that should provide a ‘base trend’ if you like, as in theory, they are the ones most likely to be realistic (mind you, those next to airports and highways must be suspect too – so even for rural sites, a full appraisal is required before use!)
It’s not difficult to see that from several thousand stations – perhaps only a small percentage could actually be of real use.
I don’t buy into the UHI biased station ‘corrective’ adjustments either – no way can such subjective and temporal aspects such as building vents, reflective radiation (from glass or tarmac, etc), be fully considered within a multi decade dataset. Just think of how much your local town/city has changed over the last few years! What about the holiday rush hour type traffic, traffic jams caused by accidents on an urban road near a station, large event parking (football matches), etc, etc – basically, think of all the temporal ‘local’ changes that could occur next to an urban station! How many are recorded and adjusted for! Not many, I’ll wager…..

“Urban areas are heavily overrepresented in the siting of temperature stations: less than 1% of the globe is urban but 27% of the Global Historical Climatology Network Monthly stations are located in cities with a population greater than 50000.”>

I am not sure where BEST got the 0.5% of the world as being urbanized but the CIA Factbook indicates that 1.5% of the approximately 30% of land surface is urbanized (I would guess this is out of date). The real issue is to define urbanized. There is a lot of controversy over what this really means or how to determine it. Using light pixels from night time satellite imagery (minus perhaps North Korea) and/or surface disruption features from day time imagery has been considered. Do suburban populations or areas where there are less than 5 people per square kilometer not fit the profile? In other words should there be an urban and rural definition. Some researchers have noted that as much as 3.5% of the land surface has been occupied as of recent times. The majority of studies seem to indicate the “real” value is probably closer to 2.4%. Bounds and definitions are very controversial. An ongoing effort on this subject indicates that with more sophisticated satellite imagery and monitoring methods a much more “firm” figure will be available to us in the next 5 years.

I do not think there is any question that there are a large number of stations in the past 30 years that were originally “rural” and are now “urban”. Many airports with stations that were far from towns 80 years ago are now mostly paved and generally surrounded by urban sprawl. The big question that I have is – How many of the present stations are found in forests and woodlands which make up 32% of the land surface? Or arable land (agricultural) – 10%, pastures 26% – deserts 14%. Or in other land types such as permanent ice, tundra, wild grasslands, steppes, mountains, rocky coasts, coral atolls, salty marshes (or other unihabitable land) that make up the remaining 15%?

The Goodbridge chart at the beginning of this post indicates that all the California station’s results, even the rural ones, are likely to be skewed by population effects. Only in last 40 years or so have we been interested in gathering climatology information from surface stations that were never meant to collect this sort of data. Can surface station data be “tortured” to determine this human climatology effect? I am skeptical.

To Paul
Can I ask you regarding the BEST data. Did they use Kriging to produce global temperature coverage? And then were these interpolation maps used to give global average temperature? If so then there are huge issues here. They need to publish the Krigiing variance maps also – as a proxy of accuracy. We may find that the confidence levels are in the order of the spatial variation in which case one cannot place any confidence in time series data accrued in this way. We need the Krige Variance maps! This will resolve the issue you’re discussing.

I am a little confused by the analysis. I am with Hector (3/30, 12:46PM) here. If the UHI effects the trend then you have to identify some mechanism or proxy for a mechanism that leads to an impact on the local temperature “trend”. Population growth is a proxy, energy consumption might be another. What the data suggests to me is that the rate of growth in population has been greater in already dense areas than in less dense areas – essentially the continued urbanization of the population – hence a higher trend. However, surely, as Hector suggests, you also have to factor in actual changes in population over the 1973-2010 period. You can see the UHI effect at work in small settlements like Cambridge Bay in the Arctic as the number of dwellings increased. Don’t you have to incorporate a similar mechanism to really gauge the strength of the UHI effect? Isn’t that what McKitrick and Michaels did?

Since Roy’s method does not take into account the amount of rural-station warming that is attributable to UHI it would seem to systematically undererstimate the amount of total warming that is attributable to UHI. One would expect the first increments of urbanization to have the strongest warming effects, so just because a station is still rural doesn’t mean it hasn’t been significantly affected by local heat sources, expecially if the thermometers tend to be placed near local outposts.

I am not sure that this is showing the same thing as McKtrick and Michaels. They had a mechanism as to why the rate of change in temperature was different in different areas, i.e., higher rates of growth in economic activities was associated with higher rates of surface temperature increase. Hector @12:46PM correctly points out that the analysis needs to incorporate actual changes in population growth – otherwise no mechanism for impacting the rate of change in the temperature has been proffered – except the implicit notion that population growth is stronger in existing densely populated areas. This makes sense but is not demonstrated and might not be true where there are few controls on urban sprawl and rural development.

Jones was involved originally in a UHI study of Chinese stations that was subject to a complaint against one of the authors (Wang IIRC then at SUNYA) on the basis that the statement that there “few if any” station moves was untrue. It was based on this flawed study that Jones concluded that UHI was not a factor. SUNYA eventuually rejected the complaint but censored the details. After Climategate I (IIRC), it came out that there had been numerous station moves of the “few if any” group, including some that required altitude corrections (also undisclosed)!!.

BEST came up with its own interesting definitions, where they classify stations as “very rural” and “not very rural:”

We defined a site as “very rural” if the MOD500 map showed no urban regions within one tenth of a degree in latitude or longitude of the site.

One tenth of a degree would be about 6 nautical miles, hardly what a disinterested observer would call very rural. IIRC “urban” was defined as more than 50% “built.” So, these “very rural” sites could be as close as only a 5 or 6 minute drive by car from places that were more than 50% built!

Large cities have large heat “bubbles” above them (UHI in 3D) that probably affect temperatures many miles from the city perimeter, depending on local environmental conditions like wind speed and direction. Had they used a stricter definition of, for example, 60 miles from “urban regions”, this might be more believable. However, even this definition is strained when one considers regions like the Washington to New York corridor, LA to San Diego, Miami to Ft. Lauderdale, SE China or mega-cities like Mexico City, Tokyo, São Paulo, etc. where I would submit there probably aren’t any stations withing hundreds of miles that would be both (a) truly “very rural” AND (b) comparable climatologically. This, I would submit, makes the use of “paired” stations to study UHI questionable.

The UHI study in Barrow, AK took a different approach. They set up a grid of identical temperature data loggers to map the temperature field around the town. This permitted the real picture of UHI for this location to be determined and separated from confounding variables like seasons, wind speed and direction and open water leads. Once they were able to map the temperature field, they were able to objectively determine which station (in the first paper) and which average of five stations (in the second paper) best represented the UHI and which station and average of five stations best represented the controls, where no UHI was evident. This took five years, after which they dismantled the rest of the network and kept up just the representative data loggers, due to the trouble and expense of maintaining the network.

I don’t believe any pairing study (other than Barrow), including BEST, has ever objectively determined that particular stations are or are not representative of UHI areas and areas not affected by UHI. Representativeness is simply assumed unquestionably. Given Anthony’s find during the surface station project that something like 80% of stations have significant siting issues, the representativeness assumption should not be accepted without support.

Given all of the above, I would submit that it is Dr. Spencer’s methodology (building on MM2007) or something similar that should be considered as the best estimate of UHI and not the other way around.

Even in the boonies many stations are near buildings, roads, dissipating electrical equipment, etc. I think most surface data are contaminated by anthropogenic waste heat and albedo mods. We should stop calling it UHI, the problem is far more pervasive and affects many “rural” locations albeit to a lesser degree than in more developed ones.

The Met office adjust the British record by .2C to account for UHI-we suspect it should be a lot more as the UK has been described as one large Urban heat island (it is relatively small with a considerable pulation density)

All in all there are so many potential flaws and blind spots in the temperature record that we shouldnt rely on it to make such far reaching ppolicy. There is an additional complications with the number of regions that are cooling, these are overwhelmned by the warming signal-likely UHI-but which disguises the complexity of the climate.
tonyb

The Other Tex said:
“Correct me if I am wrong Dr. Spencer, but my impression was that you were not comparing your dataset to the overall HadCruT trend, but to the trend in their data on a grid square basis. So if you were only comparing the HadCruT trend for that grid to the trend at the stations in that grid, it doesn’t matter if you have SH grids involved or not, right? What you have established is that UHI does in fact affect the calculated trends. A much more detailed analysis would be necessary to discern the degree to which the UHI contamination affects the entire global dataset; but what this analysis has done is significant, because it has shown the UHI contamination to be real and significant.”

Following up on a few comments about the strength of the warming bias increasing (not decreasing) with average population density, I agree this is opposite of what I expected. I don’t have an explanation for it, but I haven’t taken the time to think about it, either.

@Hugh Pepper
This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?
====================
So in your opinion assumptions made by BEST are to be preferred, and careful analysis rejected. And on top of that, throw in suggestions of conspiracy theory…

Perhaps this sort of reasoning is actually a product of working backwards from preferred conclusions?

“I tried including SH sites, Steve, but there were none that met the inclusion criteria. Also, see the update to my post…even without extrapolation, the results over the U.S. show a 57% (!) warmer CRUTem3 trend versus the low population station data.
-Roy”

Yes, the SH stations in ISH and GHCN daily are rather sparse. So, you need to add in other data sources IF you want to understand the UHI bias in the complete record. If I wanted to show the highest bias possible I would just pick a NH dataset. That’s well known.

What is wrong with treating the Southern and Northern Hemispheres as separate entities? Not only is UHI different in the Southern hemisphere, the temperature trend over the last 100 years is also quite different. The only reason I can see for averaging the temperatures of the Southern and Northern hemispheres is to disguise the fact that there is no global trend. Southern hemisphere temperatures have been almost static for a century!

“A classic goofy, alarmist half truth: Obviously only a tiny part of the world is urbanised, but equally obviously most of the world’s temperature monitoring stations are located in that tiny part. These stations then bias the results from the other circa 99.5% of the world.”

But, unfortunately, people really don’t know that. I lived in big cities most of my life and held that false impression myself. When I was twenty-five I took a job where a flew quite a bit and I was truly shocked at the amount of rural land. Now I live in the country and know first hand. CIty scientists see the earth as an endless city and need to get out of town. This urban heat island stuff is huge. Y’all need to get out in the country, and freeze your butt a little.

Another, obvious thing, something I haven’t seen mentioned here, though someone must have brought it up: That cities contains tens of thousands of windblocks called “buildings”. Wouldn’t blocking this lower wind slow down the thermostatic cooling effect that Willis E. describes so well, besides obviously giving the air more time to heat up?

Does this mean I’m gonna have to move to a densly-populated area to stay warm for the next several decades. Dreadful is the thought.

Let’s meet in Bondi and discuss the relative merits of moving closer to the equator versus living in a city densely populated by pretty young girls in bikinis. I’ll tell the missus I want to spend some time with my older son who lives there. The coffee is first class; the food at Bombay to Bondi cheap and delicious :-)

Thank you for all your hard work Dr. Spencer. It is nice to see a rigorous treatment of the subject of UHI in the CRUTem3 data set. To bad this very important work will never make it into publication.

It is a shame that science has sunk so low that journals will no longer publish anything but pal reviewed “propaganda” It seems to me we are seeing more and more real science “published” and “peer-reviewed” at sites like WUWT and Climateaudit. It maybe the saving of science.

News papers are losing market share to the blogosphere for a similar reason. People want to see something besides carefully crafted propaganda. Now the politicians want to start internet censorship. as well.

If I did not believe in censorship of journals and the news media before, the move towards censorship would have convinced me.

Steven Mosher says:
March 30, 2012 at 1:40 pm
“Yes, the SH stations in ISH and GHCN daily are rather sparse. So, you need to add in other data sources IF you want to understand the UHI bias in the complete record. If I wanted to show the highest bias possible I would just pick a NH dataset. That’s well known.”

“…understand the UHI bias in the complete record…”

Steven: Why? If that’s constructive criticism, I at a loss to see the constructive part. Are you implying that it is not possible to identify a specific urban area and then identify the UHI bias of that area? That one must someohow include Australia, Malaysia, Chile, etc in order to identify or quantify the UHI specific to an area?

To me, one should first prove there is UHI bias in the record and if possible, quantify it explicitly by region. Go build the world afterwards as I think that is how the CAGW alarmists have been masking their machinations through the baffle with BS routine. If you prove UHI for one urban area, the rest are dominoes.

I’d be careful being too loose with Dr. Spencer’s analysis. In CA there is a reason counties have low populations – they tend also to have different climate zones like mountains, deserts, military/government land, uninhabited islands, all offering low quality of life in the urban sense, and unlikely to be well-instrumented for collecting quality data for purposes of climate trending. I would also take note of the activities in CA in the early 1970’s when the BLM ran a lot of people off public lands.

If consideration of this is also part of Dr. Spencer’s analysis then a tip o’ me hat to the good professor for a job well done. If it holds up the results are devastating.

so, dp, now we should prefer urban to rural thermometer data? Those backwards rural types must be under-reporting temperatures just to spite the elitist city folks. :)

Actually, I am probably one of the very few here who was certified as an aviation weather observer, I worked at an NWS office taking hourly observations. I know something about the issues involved in temperature measurement, whether liquid-in-glass or electronic.

Because UHI warming depends on an increase over time, what your study here has shown is that the UHI warming increases faster in higher population density areas. Doesn’t this disagree with your previous results?

Yeah – I thought the same thing. If I understand your post you seem to be saying that there is no reason that an urban area should be any more affected by an UHI Trend than a rural area. I remember Roy did do a post on WUWT some time ago which graphed the UHI trends v population. The basic message was that an increase in population of say, 1000, in a rural area had a similar effect on trend as an increase in population of say, 100000, in an urban area. This makes sense to me.

I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures) unless ALL urban populations were growing while ALL rural populations remained static.

In the subsequent comments you say (at March 30, 2012 at 4:15 pm):
“Following up on a few comments about the strength of the warming bias increasing (not decreasing) with average population density, I agree this is opposite of what I expected. I don’t have an explanation for it, but I haven’t taken the time to think about it, either.”

I write to suggest one possible hypothesis to explain it.

In general, measurement sites are near the edges of populated regions (e.g. they are often at airfields).

But
(a) expansion of regions with high population density is mostly achieved by ‘urban sprawl’ (i.e. by spread of urbanisation) and is not mostly achieved by ‘infilling’ the populated area.
while
(b) expansion of regions with low population density is mostly achieved by ‘infilling’ the populated area and is not mostly achieved by ‘urban sprawl’ (i.e. by spread of urbanisation).

If this is true to some degree then the amount of urban development near the edges of populated regions (i.e. where most measurement sites are situated) would be greater for the regions with high population density.

Oooh I love this stuff, questions, questions. So if NOAA show a “cooling” trend across all their geographical zones but differeing rates for each, do Dr Spencers’ “urban” areas correspond to the NOAA zones that cooled the least and can population numbers of those zones roughly correlate to the implied UHI. Or to put it another way would the whole of the US have cooled the same if there was no urbanisation? Anyone good with numbers?

Would it be worthwhile to focus on some long term stations that meet the Cat 1 or 2 standard and have experienced transition from rural to urban if they can be linked to some nearby long term Cat 1 or 2 station that has stayed rural? Would suggest that the light density photos from satellite would be the best way to define urban/rural, as it is more infrastructure specific than population.

Would seem this approach would get away from models and be strictly observation driven.
_____________________________
Here is a set of data points that illustrate the problem. These are the only city & close by airport listed for North Carolina. The NC state population, 2011 estimate, is 9,656,401. For a comparison the New York–Newark, NY –NJ–CT Urbanized Area has an estimated population of 18,319,939 double that of the entire state of NC. The city is on the North Carolina/Virgina border and right on the ocean.

@ rgbatduke who said “Suggestions: I know you are doing apples to apples, but 5×5 degree gridding is absurd as it builds in a horrendous projective correction near the poles, and one that is reasonably accurate only near the equator. I’d strongly suggest building an icosahedral tiling of the sphere at a scale-adustable granularity.”

In your post at March 30, 2012 at 4:40 pm you say;
“To (sic) bad this very important work will never make it into publication.”

It HAS made it into publication. It has been published on WUWT.

And please do not provide any BS about the ‘worth’ of scientific work being related to peer review prior to publication. It is not.

A third-rate patents clerk published two papers on (what he called) relativity. The worth of those papers is demonstrated by their having revolutionised physics, and the fact that they were not peer reviewed prior to publication does not change that.

Two bicycle salesmen published a seminal paper on aviation in a magazine about bee-keeping. The worth of that paper is demonstrated by commercial air traffic, and the facts of its lack of peer review and where it was published do not change that.

etc.

Science assesses information on its merits. The source of the information is not relevant (nullius in verba).

This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?

That would be all well and good if the stations were spread evenly over the globe, but they are not. Many are near urban centers because that’s where they can be serviced more conveniently.

“I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures) unless ALL urban populations were growing while ALL rural populations remained static.”

I actually agree with John Finn here. Let’s look at the long term global temperature trend line since the LIA:

As we see, the trend has not accelerated. If it had it would have exceeded its long term parameters by now.

Therefore, the ≈40% rise in CO2 has had no measurable effect. It’s right there in the record. If the runaway global warming predictions were right, we would see a recent rise in the long term trend. But we don’t [note that the green long term trend line is slowly declining].

That long term temperature trend line is the same whether CO2 was 280 ppmv, or 392 ppmv. Does that not cause a major problem for those claiming that “carbon” is rapidly pushing up the temperature? But the long term trend is indistinguishable from natural variability. And no one has falsified the hypothesis that the observed temperature changes are the result of natural variability.

The planet is simply warming from the LIA — one of the coldest episodes of the Holocene. A warming ocean outgases CO2. And the geologic record shows conclusively that rises in CO2 follow temperature rises, not vice versa.

Thank you Dr. Spencer for a most enlightening and wonderful paper. Real analysis of real data — an increasingly novel idea it seems.

Please don’t give up on publication. I realize how frustrating and maddening it must be battling those who choose to place agenda before facts. But keep up the fight because the facts will prevail in the end.

I would love to write this work up and submit it for publication, but I am growing weary of the IPCC gatekeepers killing my papers; the more damaging any conclusions are to the IPCC narrative, the less likely they are to be published. That’s the world we live in.
==================================================
Dr. Spencer, I think it’s time you and some of the more responsible adults simply start ignoring the current lunatic infested journals. And, simply publish in the alternative places. Your work on this issue (UHI) and others is so well known, very few alarmists actually dispute it, they simply hand wave and ignore this glaring fact.

Perhaps some of you guys can set up a publishing network of your own….

Anymore, I only use published studies on the climate issue to be held up for scorn and ridicule….. as do most here. My recent favorite was the one about if the stuff making us cooler wasn’t happening, we’d still be getting warmer!! I’m thinking up publishing a study about if frogs had glass asses they wouldn’t jump so high. But, then there’s always the lack of arctic ice causing all the snow, when before it didn’t, but it does now…. that we’re at a 7-8 year high in the ice extent.

You really shouldn’t risk tarnishing your name and reputation by publishing in some crap magazine anyway. I understand there are different venues available today.

Hugh Pepper says:
March 30, 2012 at 12:46 pm
“This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?”

Roy Spencer says:
March 30, 2012 at 4:15 pm
Following up on a few comments about the strength of the warming bias increasing (not decreasing) with average population density, I agree this is opposite of what I expected. I don’t have an explanation for it, but I haven’t taken the time to think about it, either.

I think the problem here is that UHI itself is widely misunderstood. It’s assumed to be greater heat release and retention in urban areas.

I think decreased urban aerosols and aerosol seeded clouds since the 1970s is the primary cause of urban warming since that time in the developed world (quite different trends exist in the developing world). This explains why larger urban areas show a greater trend, because aerosol concentrations are largely a function of the size of an urban area. It also explains why the effect is smaller in the southern hemisphere, because aerosol levels in the SH (ex tropics) have always been lower than in the NH (Brazil possibly excepted).

there are hundreds of thousands of data files in the NCDC ISH archive, each file representing one station of data from one year. The data volume is many gigabytes.

I did try and download this data but gave up under the sheer immensity of the task. If you could make it available in a more accessible form, I and I expect others would appreciate it.

One final point, I believe that an analysis of this data by time of day would produce some interesting results. I believe you will find the warming is predominantly a daytime phenomena. This would be directly contrary to analyses based on min/max temps, which IMO wrongly attribute increasing min temps to increasing nighttime temperatures, when the cause is increased solar insolation due to decreased aerosols.

The great dying of the thermometers can not be explained in terms of too much data to analyze, as computing power during this time grew by leaps and bounds.

If I was tasked to find a human signal in global temperature, the killing of thermometers that showed no warming would be an obvious step to take. The then fudging of the UHI is an easy cover as no effort was taken to show its full extent and an ever upward temperature signal can be foisted on the world.

The poor dears at the moment are having trouble finding enough fudge factors to keep the temperate level, as even the UHI is not enough to cover the decline. The coming years will be such a disappointment to the poor souls.

I think that either Dr. Abdussamatov is right and the year 2014 will show transition into a 200 year “Little Ice Age” or Professor Vladimir Paar is right and it will be a new glaciation era of 70,000 years. Both could be right. We will never know since our lives are so short.

Dr Spencer,
One thing that appears to be missing is the distribution of trends in each class. How significant are the differences between the trends, compared to the standard error of the trends?

It would be interesting to see your opinion of why your result is different from the BEST esitimate of the UHI, which is based on a difference between all the very rural stations, known to be distant from population centers, and the rest of the world’s stations. Using a larger number of stations they got and uncertainty estimate of +/- 0.19C/century for the slope.(95% confidence)

so, dp, now we should prefer urban to rural thermometer data? Those backwards rural types must be under-reporting temperatures just to spite the elitist city folks. :)

I was confident you had it under control but as a fellow aviator who earned his wings at John Wayne Field (long before it was JWF), I’ve seen first hand why a lot of areas of California are sparsely populated and know how quickly one can transit climate zones in the Golden State. From the air the highest and lowest points in the state can be seen at the same time as they’re not too far apart, and not far from either are high and low deserts, the central valley, glaciated mountains, sparse coastal regions, etc. I’m looking forward to learning how it is all quantified in your paper. Thanks for responding.

Dr. Spencer,
If you’re still reading this thread, please expound on this quote Steve Goddard says is from you. I have asked Steve Mosher to comment on this a few times, but apparently he didn’t read it.

Which makes me think an analysis by size of urban area would reveal more about UHI than an urban – rural comparison.

Although irrigation isn’t exclusively a rural practice. Here in Perth, Australia, when they moved the official Perth site from opposite an irrigated park to a non-irrigated field a couple of Ks away, nighttime temperatures immediately rose 1.5C. This is because the park was irrigated at night, increasing the thermal capacity of the air and decreasing nighttime temperatures.

dp says:
March 30, 2012 at 6:33 pm
I’ve seen first hand why a lot of areas of California are sparsely populated and know how quickly one can transit climate zones in the Golden State. … I’m looking forward to learning how it is all quantified in your paper. Thanks for responding.

Did you not notice that the plot of California sites was labeled ‘Goodridge 1996’ and was not part of Dr. Spencer’s analysis?

It is disappointing to see this arguing over atmospheric temperature when we should be measuring Earth’s heat budget. Atmospheric temperature is not a metric for heat content. Averaging this incorrect metric only makes things worse.

Is there any chance that someone somewhere will actually measure the heat content of the air in Kilo Joules per Kilogram, the correct metric if there is concern about ‘trapped heat’? If the humidity (which is normally reported at the same time as the air temperature) is known then calculating the enthalpy and therefore the heat content is simply done.

I have a feeling the tropical mid tropospheric ‘hot spot’ is not there as the enthalpy of the air is high therefore it takes a lot more heat energy to raise the atmospheric temperature and the GCMs are not programmed for enthalpy.

This is relatively basic physics, but seems to be too complex for climate scientists – or perhaps like the air-conditioning in the congressional meeting room, they are using it because it is useful even if they know it is wrong – and certainly fools people, even on this blog few are calling them on it.

Philip Bradley says:
March 30, 2012 at 5:47 pm
……….
I think the problem here is that UHI itself is widely misunderstood. It’s assumed to be greater heat release and retention in urban areas.

I think decreased urban aerosols and aerosol seeded clouds since the 1970s is the primary cause of urban warming since that time in the developed world (quite different trends exist in the developing world). This explains why larger urban areas show a greater trend, because aerosol concentrations are largely a function of the size of an urban area. It also explains why the effect is smaller in the southern hemisphere, because aerosol levels in the SH (ex tropics) have always been lower than in the NH (Brazil possibly excepted)……..

Well first there is the effect of all the buildings and concrete and tarmac that reflect a lot of heat and also get hot and act as heat stores for overnight. Then the buildings have air-conditioning heat pumps dumping waste heated dry air to the atmosphere or heating leaking heated air to the atmosphere. All the power being consumed normally turns to heat and leaks from the buildings and lights. Then most cities have a lot less plant life trees, grasses than the rural areas. Plants in rural areas transpire water into the atmosphere raising its humidity and therefore its enthalpy and increasing the amount of heat required to raise the temperature. So the dryer air over towns and cities will be warmer for the same amount of energy and the amounts of energy available in cities at night is higher for the reasons given.

And of course – atmospheric temperature is in any case the incorrect metric to measure atmospheric heat content.

Now that you have done this, and shown your main message, you might do two things and then submit to a statistics journal like Annals of Applied Statistics: then instead of an outright rejection, you might get a debate such as the debate stimulated by McShane and Wyner.

the two things:

1) address Steven Mosher’s points in detail — it seems from your responses that you can, and that they do not affect the main message;

2) if it isn’t too much trouble, follow this up with analyses of the 4 time points separately, to see if you can discover at what time of day the effect is greatest. I think that it will be a really good idea for climate scientists to step away from the “daily mean”, and look at particular times of day. Usually (I don’t know if it is true in this case), doing the disaggregated analysis after doing the aggregated analysis is not that much extra programming time, but is a lot of additional reading and writing time.

I think this would be worthwhile, but that is only an opinion from a non-expert.

… all the very rural stations, known to be distant from population centers, and the rest of the world’s stations.

According to BEST, “distant” can be as close as 6 nautical miles (1 tenth of a degree of latitude or longitude). That hardly qualifies as being very far from population centers. Add to that the point made by Philip Bradley regarding irrigation and, at best, the “very rural” are a mixture of sites with anthropogenic influences of some sort and probably a minority that have minimal anthropogenic influences. This makes their “conclusions” questionable. Furthermore, BEST, to my knowledge, has not been published in a peer-reviewed publication and they have not really addressed the issues raised on this august forum (see links here).

Left out of discussions above is heat released from burning fossil fuels which have totally released energy to warm the atmosphere by .2 C. Touched on was irrigation in CA that increased the atmosphere temperature by 3 C. However, worldwide there is about 900 cubic kilometers per year of fossil ground water from slow or no recharge aquifers being produced, which has resulted in increasing the temperature of the atmosphere by about 1.6 C per year since 1950. Also left out is the release of energy from condensation at night on grass blades used for lawns and production of food and fodder. Area extent of both urban and rural grasses has increased steadily since roughly 1950.
It appears to me that there is a temperature control process at work in the upper Troposphere.

Smokey says: “If the runaway global warming predictions were right, we would see a recent rise in the long term trend” Smokey: those “predictions” are “projections” and projections are logically neither right nor wrong.

NH = Northern Hemisphere, it’s not exactly an isolated region is it, you know, one whole half of the Earth. If it’s significant in the NH and non existent in the SH then it’s still significant over all.

Give constructive feedback where it’s due and let him further improve the analysis. That’s science. Some write as if anything that isn’t perfect gets tossed in the bin.

They try to estimate numbers of non-existent climate refugees but something as important as UHI is neglected. Go figure.

What does “rural” actually mean in 2012. My rural locale, the Fraser Valley is arguably the best farm land in Canada. It produces mouth-watering corn, delicious blueberries and all manner of veggies and livestock but it also has the Trans Canada Highway running through it, land fills, two international airports, acres of natural gas heated greenhouses, bio gas plants, composters, chicken, turkey, pig and mushroom “farms” – steel sheds – and enough diesel powered combines, tractor-trailers, locos and tractors to give Gore a heart attack. With mechanised farming i would think that the number of people working farms has dropped as production has increased over the last 50 years. The measure of the “UHI” of rural areas by population density is, i’ll wager, underestimating warming effects of intensive,energy -dependant, modern farming and “rural” life.

At the time of this post (after 9.18 pm WUWT time) Hugh Pepper had not responded to those commenting on his observations about the BEST data. This is not surprising given his obvious misunderstanding, whether due to genuine incomprehension of or deliberately ignoring, the effect of UHI on the temperature records and how this has affected and is affecting political decisions. A number of posters have commented on the southern temperature records and have recommending using data from Australia and New Zealand. You all should be aware that the temperature data records in both of these countries have been recently “modified” with some stations, usually rural. being excluded. In other cases the modifications appear to be upward. I am not aware of any downward modifications but this doesn’t mean these have not occurred.

Roy said:
“I would love to write this work up and submit it for publication, but I am growing weary of the IPCC gatekeepers killing my papers; the more damaging any conclusions are to the IPCC narrative, the less likely they are to be published. That’s the world we live in.”
___________________________

A proposed new procedure to publish scientific papers and conduct peer review:

Roy, you may recall that in early 2008 I asked Joe d’Aleo to published a paper for me on icecap.us and he kindly did so. I published all my data and calculations and my paper was critiqued in ClimateAudit.org by Willis and others. I believe this ad-hoc process constituted a far more rigorous peer review than the typical “pal review” that the hockey team and other global warming acolytes apply to their friends’ papers in the major journals.

Technology has made these journals and their review methods obsolete anyway. Why don’t you, or Anthony or Joe, etc. establish a website specifically for parties to publish their climate research papers and have them critiqued – the rules could be simple – publish your paper with all data and all supporting calculations. Anyone can critique your paper provided they mind their manners, dot their i’s and cross their t’s. No gates and no gatekeepers. I expect that you would soon leave these once-prestigious journals and their gatekeepers in the dust.

@JFD
“It appears to me that there is a temperature control process at work in the upper Troposphere.”

Well done sir !!

Since the atmosphere is a “regulator” operating under, (for the most part), the combined gas laws,
a rise in total atmospheric temperature must either increase the atmospheric pressure, (only possible locally), or increase the atmpospheric volume. Now since the tropopause could be considered the point at which convection stops, this means that the tropopause must raise slightly in altitude (I believe this has been measured ?) This will greatly increase the surface area available for radiation loss thus maintaining the system balance.

The whole thing is controlled PURELY by atmospheric pressure and incoming radiation. The lapse rate is controlled by the combined gas laws, La = Cp/G, (Cp = specific energyof the atmosphere), the only thing that can change the lapse rate is atmopheric condensation of H2O, which lowers the lapse rate because it increases Cp), but the energy transfer rate is still the same !!

If a parcel of air has more energy than it can hold wrt the air above it, it MUST rise. Its called convection, and along with conduction is the dominate method of moving energy within the atmosphere.

Thus, since no gas (apart from H2O condensation, which is really a phase change) can change the lapse rate (certainly NOT a trace amount of CO2), there can be no greenhouse effect in the Earth’s atmosphere.

Dr. Spencer,
I think your analysis understates UHI because the period you analyzed was one of the steeper sections of the record since it occurred from the bottom of the trough (mid-’70’s) to present. By analyzing this section, your baseline slope is much steeper than the overall long term trend, so any absolute UHI °C/yr identified will be a smaller % of the long term slope in that section.

You identified a method by which UHI goes from 15% to 30%. I identified a difference in slope of 2.5x after cyclical elements were removed. I went ahead and posted my own analysis of CRUTem3 tonight so you could see the details. Please see my analysis here:http://naturalclimate.wordpress.com/2012/03/31/northern-hemisphere-uhi-crutem3-18/
Based on our two studies taken together, and then taking the idea of using all 5 population density categories, I pose the question on whether between the two of us we have identified UHI as MORE THAN 100% of the total observed temperature increase. I don’t thing it is out of the question. I would be most interested to hear your perspective on this. As always, thanks again for your interesting articles. Good show on Stossel.

As far as giving up on the gatekeepers, I’m thinking the gates they are guarding aren’t worth walking through anymore. Somebody might see you. Mike S.

One problem that Dr. Spencer will have to address is the lack of spatial coverage in his approach.
Effectively his selection of grid cells is biased to select cells that must have all three population types. This does two things

1. It limits you to NH where UHI is worse
2. It eliminates grid cells that are only rural, cells that have rural and medium population
and cells that have rural and urban but no medium population.

The spatial coverage is consequently very small.

Here is another way I can illustrate the problem.

In the Berkeley Earth Data ( which includes the data Dr. Spencer uses plus more) there are roughly 36K stations that pass data quality checks ( basically have more than a few months of data )

Of those 36853 stations: 15,348 are in Dr. Spencers LOWEST population class, using his
population dataset ( GrumpV1) 12797 are in his “medium” population data class.
and 6471 are in the high population dataclass.

For those of you doing the math ( 12.7+15.3+6.4 =34.4) there are OVER 2000 additional stations that have “missing”
population data. These are “missing” because they are stations that are
a) bouys
b) in antarctica
c) Atols
d) stationary ships at sea.

So, basically 17500 or roughly 50% are in the lowest class.

If you compare the lowest population to the highest population you will get an estimate of
what the bias is for any give pair. However, you have to look at the fraction of stations that actually have that bias. If the actual number of high population stations is significant
then the total bias will be high. But if there are very few stations that have high population
then the bias between high and low, WHILE REAL, will not pollute the entire sample.

Lets use Dr. Spencers estimate to work with

At the lowest population we have a trend of .22C decade
At the medium .24C decade
at the max .28C decade

As Zeke and I noted this difference between the high and the low ( .28–.22)
is fairly close to what we found in our AGU work ~.04C decade. In fact,
In some of our sensitivity work we could find differences as large as .06C
But to see that we had to really restricted the spatial coverage ( <25% of the earth)
basically we had to compare the best to the worst.

When you look at a more complete dataset than ISH you get
50% of the stations are Spencers low population
33% of the stations are medium population A bias of .02C decade
17% of the stations are high population a Bias of .06C decade

Weight those biases by sample and you get an idea of the bias in the whole sample.
17% of your sample will have a Bias of .06C, 33% will have a bias of .02

If you are concerned about Bias in the lowest population class…
there are roughly 8000 stations with population density less than 1. That is,
More stations with zero population than stations with populations over 500 people per sq km

Ian W says:
March 30, 2012 at 8:12 pm
Well first there is the effect of all the buildings and concrete and tarmac that reflect a lot of heat and also get hot and act as heat stores for overnight. Then the buildings have air-conditioning heat pumps dumping waste heated dry air to the atmosphere or heating leaking heated air to the atmosphere. All the power being consumed normally turns to heat and leaks from the buildings and lights. Then most cities have a lot less plant life trees, grasses than the rural areas. Plants in rural areas transpire water into the atmosphere raising its humidity and therefore its enthalpy and increasing the amount of heat required to raise the temperature. So the dryer air over towns and cities will be warmer for the same amount of energy and the amounts of energy available in cities at night is higher for the reasons given.

I should have my point clearer. I was referring to any UHI trend.

If you look at power consumption, per capita USA energy consumption peaked in 1973. So there is no contribution to the trend from this source during the period of Dr Spencer’s study. Although there may be a contribution from increasing population densities, where this has occured.

Roy’s “middle graph” shows almost 0.2C difference due to UHI effect in ~30 years.

In 2008 I calculated a similar difference of ~0.2C as the difference between global Hadcrut3 Surface Temperatures (ST) and UAH Lower Troposphere temperatures (LT), also in 30 years (I thought this point was also made by Roy or someone else above, but I cannot find it now).

I concluded in 2008 that there was a probable warming bias of ~0.07C/decade in the ST data, since at least 1979, if not earlier. I know it’s somewhat “apples and oranges” because of different altitudes, but still the numbers appear close.

I found it good to see many different approaches producing basically the same result. My page also shows evidence of the UHI effect increasing more rapidly in very rural areas urbanizing slightly over time, than in urban areas.

Okay. There is an UHI bias in land temperatures; this conclusion does not seem to be controversial. By looking at ocean data — whether via CruTemp or via UAH — we have over 0.1 C increase per decade over the last three decades. Any comments on that other than the increase has softened in the last several years?

The above article by Roy Spencer proves that
(a) UHI provides significant error to temperature trends in gridded localities
and
(b) the degree of the error relates to population density in the regions.

But at March 31, 2012 at 12:17 am you yet again assert;

“One problem that Dr. Spencer will have to address is the lack of spatial coverage in his approach.”

And the remainder of that post is expansion of your assertion which you first made (but less clearly) at March 30, 2012 at 12:36 pm and have repeated several times since.

I am surprised that you have repeated and pressed the point because it was completely refuted by others after you first made it.

I think ‘atheok’ provided the most clear refutation at March 30, 2012 at 4:43 pm where he asks and says;
“Are you implying that it is not possible to identify a specific urban area and then identify the UHI bias of that area?”
And
“To me, one should first prove there is UHI bias in the record and if possible, quantify it explicitly by region.”

So,
Dr. Spencer DOES NOT have to address the lack of spatial coverage in his approach because it has no relevance of any kind to his analysis.

If you want to extend his analysis to include the entire globe then do so because Dr Spencer’s analysis demonstrates that such an extension would have value. And that demonstration is the value of his analysis.

Simply, your carping is like telling the Wright brothers that they needed to expand their work to provide a Boeing 747.

An Inquirer says:
March 31, 2012 at 2:29 am
///////////////////////////
I have long held the view that the land based temperature data should simply be consigned to the bin. The reasons are fourfold.
First, given the heat capacity of the oceans which dwarfs that of land air temperature, it is ocean temperature not land air temperature that is important.
Second, it is ocean temperatures not land temperatures that drive climate (this is something that follows from the first point).
Third, the land air temperature data does not even measure the correct metric. It tells us nothing about energy.
Fourth, the land air temperature record is almost certainly corrupted by UHI.

The problem with sea temperature is that we do not have good quality data on it and are only just beginning to compile this. We therefore cannot reliably answer the enquiry made by An Inquirer.

The oceans are the key to global warming. Without the oceans warming there can be no global warming. Understanding the reasons why the oceans may be warming (if they are) is also the key.

As I have often observed, not sufficient thought has been given to how DWLWIR works over the oceans. For example:

1. Do the oceans reflect some part of the DWLWIR, if not why not?

The K & T energy diagram shows that not all the solar is absorbed by the surface and some part of this is reflected. The reflected part is predominantly from the oceans which reflect solar when striking at low angles of incidence. Some part of the DWLWIR must strike the oceans at a similar low angle of incidence, so why is that part not simply reflected?

2. The optical physics is such that 20% of all DWLWIR is absorbed within 1 micron of water and 60% of all DWLWIR absorbed within 4 microns of water. Theoretically, there would be so much energy being absorbed within the first few microns of the ocean that this would drive a huge amount of evaporation which is not being observed. Why not? For example, is it because much of the DWLWIR is simply being reflected?, or perhaps it lacks sensible energy? Some explanation is required.

Following on from point 2, given the absorption characteristics of water, water is essentially a LWIR block (much like sun cream may block harmfull UV rays), how does any energy from DWLWIR effectively heat the ocean? How does it find its way down from the top micron level? Indeed, does not wind swept spray/spume in itself act as an effective block. IF wind swept spray/spume is more than 4 microns thick it in itself would capture 60% of all DWLWIR and would carry it away in the air before it even gets to the bulk ocean.

I am merely pointing out that there are substantial issues with DWLWIR and the oceans and much consideration needs to be given to this. To date, I do not see that proper consideration has been given to the optical physics involved and the consequences of this..

“I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures)….”
I think Philip Bradley and Ian W. may have already touched on this point, but..
I can’t actually see why I would expect them to be similar, unless they were identical in physical composition and thus identical heat capacities. A lake or forest would be expected to be very different from concrete or asphalt. The former would rise less with increases in radiative input, and I would not expect cooling due to evaporation to change linearly with respect to other heat redistribution processes.

A second point that I have not seen addressed is a wider one concerning Carbon dioxide. It may be considered “a well mixed gas” globally, but its production by human beings certainly is not. Is it not produced primarily where there are significant human populations? How then, do any GCMs claiming to model global temperatures wrt CO2 deal with this? It seems to me that every thermometer also needs to be measuring local CO2 concentrations.

When I want an “easy to play with” dataset, I head to NCDC. I’ve yet to find an easier access point than at the bottom of this FAQ, “The Global Anomalies and Index Data” section. I looked at “The Annual Global Ocean Temperature Anomalies (degrees C)“. The set goes back to 1880. The anomaly baseline is the 20th century average (1901-2000). To easily feed into a spreadsheet, open in a word processor, find and replace all double spaces with single spaces, repeat until no double spaces left, then cut and paste into a spreadsheet (paste special) with a space as delimiter. Since you said “last three decades” and the last full year is 2011, I’ll do calculations for ranges with the last year ending in 1.

Sure enough, 1982 to 2011 was 0.12°C/decade. 1972 to 2001 was the same.

But the winner is 1912 to 1941, 0.13°C/decade, the highest three decade group in the record. So nothing unusual is going on.

That highest rate was nearly matched in 1922-31 with 0.22°C/decade, and blown away with 1932-41 at 0.33°C/decade. Note also there are sharp drops, as 1942-51 was –0.30°C/decade. Again, nothing unusual going on. Nothing has happened in the last three decades that is outside the range of natural variability.

For comparison, using Dr. Spencer’s range above of 1973-2011, the rise was 0.12°C/decade. This appears to match the rate in the chart in the Update for US Temperature Trends, lowest population density group, derived from NCDC land data. Which is noted for entertainment purposes only.

Steven Mosher says:
March 31, 2012 at 12:17 am
One problem that Dr. Spencer will have to address is the lack of spatial coverage in his approach.
Effectively his selection of grid cells is biased to select cells that must have all three population types. This does two things

1. It limits you to NH where UHI is worse

If one cannot do a comparison in the southern hemisphere because the data do not contain all three population types, how can one say for sure that NCW (non-climate warming) is worse in the NH? Are you just basing this on the fact that there’s a higher percentage of land in the NH? Can’t microclimate factors also affect stations with very few people?

I see Dr. Spencer’s research as saying that NCW affects higher population areas more than low population areas, but that’s just the difference above any baseline NCW.

Without an analysis of the statistical significance of the trends calculated Spencer’s grid method, we can’t determine whether the analysis is meaningful. Lack of statistical significance in the differences in the trends between population density groups could render the discussion moot.

This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?
*****

That’s an irrelevant point. The real question is what is the percentage of sites located in these “negligible” urban areas. My guess is that it’s shockingly high.

richard verney says:
Richard, you simply forgot that the same few microns absorbing DLWR are also emitting ULWR, and the balance of this LWR, along with some evaporation and heat transfer up and down with the lower water, and heat transfer with air above, results in the balance of temperature.

Eric Adler mumbled as if following a script on March 31, 2012 at 6:56 am:

Without an analysis of the statistical significance of the trends calculated Spencer’s grid method, we can’t determine whether the analysis is meaningful. Lack of statistical significance in the differences in the trends between population density groups could render the discussion moot.

From Dr. Spencer’s article:

Depending upon how low in population density one extends the results, the level of spurious warming in the CRUTem3 dataset ranges from 14% to 30% when 3 population density classes are considered, and even 60% with 5 population classes.

Dr. Spencer found discrepancies well exceeding 10%, with the high end more than twice the low end, and you think mouthing the magical incantation “statistical significance” will invoke the protection of the Climate Gods and miraculously preserve the integrity of the CRUTem3 dataset? Urgent message from Planet Earth to Adler: Try harder.

Mosher: “50% of the stations are Spencers low population”
If those low population stations are overrepresented in the US and underrepresented in the rest of the world it would explain why the trend is lower in the US and higher in the rest of the world (or at least northern hemisphere)
Isn’t that the case? As I recall Steve McIntyre once mentioned that most stations in the world outside the US are Urban or located at airports..

Roy Spencer says:
The Urban Heat Island (UHI) effect is familiar to most people: towns and cities are typically warmer than surrounding rural areas due to the replacement of natural vegetation with manmade structures.

Henry says:
How about if what you are seeing and measuring could be (in many cases) exactly be opposite to what you think?
Let me give you an example. Las Vegas used to be desert, and indeed in 1973 I suspect it still was mostly desert country.
Note the following results from Las Vegas Intl. Airport ( lat. 36.08):
Maxima rising at 0.16 degrees C per decade since 1973
Means rising at 0.524 degrees C per decade since 1973
Minima rising at 1.022 degrees C per decade since 1973

I have observed this before and it was in sparsely populated northern Namibia. I know what is happening. It is the increasing vegetation – THAT IS PLANTED – AND, SOMEHOW WATERED BY MAN, that is causing this particular warming. This is the biggest controversy that I have observed since my investigations into global warming began: if you want earth to be green, then some heat is going to be trapped by the increasing vegetation.

Gail Combs says:
March 30, 2012 at 5:07 pm
Here is a set of data points that illustrate the problem. These are the only city & close by airport listed for North Carolina. The NC state population, 2011 estimate, is 9,656,401. For a comparison the New York–Newark, NY –NJ–CT Urbanized Area has an estimated population of 18,319,939 double that of the entire state of NC. The city is on the North Carolina/Virgina border and right on the ocean.

Take a look at the city vs the airport! Norfolk City and

Norfolk International Airport
………..
The two trends are very different for two locations close together, not sure of reason. Both are airports. The Norfolk airport is closer to the Atlantic Ocean but the Norfolk Naval Air Station (Norfolk City/NAS) is directly next to the Hampton Roads (mouth of the James River). I expect the NAS is about as urbanized as it ever was, WWII being the time it was really built up and the beggining of the record and the Norfolk Airport having slower growth and surrounding urbanization over the same time period. The Oceana NAS located closer to the Atlantic than the Norfolk airport seems to have a similar trend to Norfolk NAS. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425723080030&data_set=14&num_neighbors=1, I beleive it also had most of its growth during WWII.

Has anybody looked at geographic effects on temperature-trends? If not, then it could be a matter of urbanization or it could be a matter of the sorts of geography which lead to it. For example, most big cities are on land, coastal or along a river near the sea, on or near large regions of arable land, etc. It would be good to see a study of temperature-trends in regions geographically similar to typical sites of cities compared to those of cities.

On another note, in terms of adapting to changes (rather than trying to stop them), does UHI matter that much? I mean assuming there is some change happening, natural or otherwise, if it is large in urban areas then most of humanity will have to adapt regardless of whether it is large or small elsewhere.

Terry Oldberg: Smokey says: “If the runaway global warming predictions were right, we would see a recent rise in the long term trend” Smokey: those “predictions” are “projections” and projections are logically neither right nor wrong.

Hi again.

Projections can be accurate or inaccurate, with respect to publicly available standards of accuracy. If the runaway global warming projections were accurate, we would see a recent rise in the long term trend.

“I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures) unless ALL urban populations were growing while ALL rural populations remained static.”

I’m impressed that a lot of apparently bright folks are missing a simple point in Roy’s work. The fact that you can categorize the “slopes” of the temperature record by population size categories is powerful evidence of UHI affecting the record, whether or not you “… can’t see any reason why urban TRENDS….”. Maybe it would help you see the power if you were to provide a bunch of unnamed station records and have Roy estimate the population range of the site.

People like Hugh Pepper “are the conspirators” who put many people seeking the truth on the defensive. Mr. Pepper, there is nothing wrong with seeking the truth. As a project engineer and designer in a multitude of process automation areas, I naturally question things to find out what the data and results are telling us. It’s amazing when you don’t assume you have all the answers at the onset of a project. This is far different than your scenario just accepting that using data in ways that can show a preconceived result must be the best way.

In my experience, there is much that can be learned by managing projects if one is open to the science learned along the way. In your majority world of sheep, there is a very strong bias to seek data which proves the hypothesis and strongly disregard data which shows there is something else going on.

All modern data is also biased by the fact that digital temperature sensors respond faster to temperature changes. A momentary breeze of hot air, say from tarmac heated air at an airport, will register as a higher peak temperature on a fast response digital sensor as compared to an old fashioned mercury in glass min/max thermometer.

Wait, wait, I am lost. You start talking about the heat island effect, but your study had nothing to do with the heat island effect (which the CruTem3 average takes into account. Presumably you have problems with how they did it. What are those problems?). INCREASE in density would of course result in spurious warming, but MORE density would not (in fact between 1970 and today I would guess high density correlates NEGATIVELY with density increase). So your explanation for the effect you are seeing fails. Is there an explanation that is consistent with Anthropogenic Warming? Well, I though of one in the first five minutes: low density in the US is mostly high altitude: Maybe temperature increase has been higher at lower altitudes? Maybe not, but at least I have attempted to come up with an explanation for the effect, rather than distracting my audience by talking about something utterly unrelated!

This paper is consistent with the results of my 2010 analysis of temperature data for Australia, comparing our Bureau of Meteorology’s (BoM) official temperature record with

(1) data for all 43 long record temperature stations (LRTS) in rural locations and
(2) data for known urban heat islands (UHI), Sydney and Melbourne.

Relevant results, in summary, are
1. Warming according to BoM is more than twice that of rural LRTS (0.5/0.22 deg C)
2. Warming according to BoM conforms with that of a known UHI, Melbourne (0.5/0.51 deg C)

I have just looked at CRUT4 vs CRUT3 differences for some Australian 5 degree grid cells. Found this stunning adjustment for the Murray Darling Basin grid immediately west of Sydney.
Instantaneous moving of the Sydney UHI several hundred km west.

I think you guys did not yet get it. It is not only buildings that cause UHI. It also when places like Las Vegas and Johannesburg that used to be desert or semi desert (no trees) get water from afar and are turned into green gardens with lots of trees and plants and gardens and golf courses. I saw the same thing in northern Namibia where a dead river started flowing again and where there now is a big increase in in greenery in the northern part of the Kalahari desert, see here:https://wattsupwiththat.com/2011/03/24/the-earths-biosphere-is-booming-data-suggests-that-co2-is-the-cause-part-2/

I am only a fan of the climate debate and have no qualifications to perform an informed debate but could someone tell me why, when Hugh Pepper said “They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average.” he was roundly criticized for having missed the point that there are more stations, more thermometers in urbanized areas than rural.

Why should the density of thermometers per square kilometer make any difference whatsoever? Is a square kilometer with a dozen thermometers somehow warmer than the same area with one?

ABE3 says:
Why should the density of thermometers per square kilometer make any difference whatsoever?

Henry says
The problem is that in the 99.5% where there is no people living, there are no thermometers. So all weather stations taken together are “skewed” to read the UHI effect, including some extra plantation of vegetation by man that is also contributing to the entrapment of heat, as explained earlier.

Henry says
The problem is that in the 99.5% where there is no people living, there are no thermometers. So all weather stations taken together are “skewed” to read the UHI effect, including some extra plantation of vegetation by man that is also contributing to the entrapment of heat, as explained earlier.

That is certainly an exaggeration. There are not “no thermometers”. There are less of them. However, you suggest that rural temperatures are being erroneously interpolated from urban thermometers. It is my understanding that limts are commonly used as to the distance across which temperature data are interpolated. That would seem to restrict the rural area to which urban readings would be applied.

I also wonder, if the global warming trend is simply the product of increasing urbanization:

1) Why did Peterson 2003 find so little UHI effect in general and Jones 2008 found no signal at all from increasing urbanization in China?

2) What is causing the increase in ocean heat content?

3) What is causing the dramatic air temperature increase in the Arctic?

1) I honestly don’t know and I would not trust any work unless I had made sure about it myself. There is some indication that (some) results reproted by UK and USA could have been compromised.http://letterdash.com/HenryP/what-hanky-panky-is-going-on-in-the-uk
2)see: http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
My latest tables clearly imply that the observed warming over past 4 decades was largely due to natural causes. Either the sun shone a bit brighter or there were less clouds. Or there was less ozone , which could have had a human influence but not anymore (?) , shielding us. There are various theories.
Looking at the differences between the results from the northern hemisphere(NH) and the southern hemisphere (SH), what we see is happening from my dataset is that more (solar) heat went into the SH oceans and is taken away by water currents and/or weather systems to the NH. That is why the NH is warming and that is why the SH does not warm.
3) A lot of the extra heat coming into the SH oceans condenses in the arctic, because of the cold there, releasing energy.
(when water condenses it releases heat)

Abraham3, in some data bases, Arctic air temperatures are actually not Arctic, they are land based. The grid over the Arctic is filled in with land temperatures. Other data bases use other methods. The various methods of filling in the Arctic grids make quite a difference in the final data shown to us, if you consider digits far to the right of the decimal point.

However, artifacts of method is not a significant contributor to real Arctic temperatures. Arctic Atmospheric Pressure Oscillation conditions and oceanic currents provide the lion’s share of temperature anomalies across the Arctic as it yearly cycles through the pattern of solar heating available to it. Anything else, be it method artifact, Sun anomalies, or CO2 increase- variables that only very slightly and only mathematically, not measurably- affects Arctic temperature is buried minutia.

Day to day Arctic temperature trends, thus statistical averages and year to year trends, are highly and significantly correlated to natural oceanic and atmospheric Arctic conditions. Period. In fact any trend in temperature anywhere in the world is also so correlated to the oceanic and atmospheric conditions there. And not a single AGW scientist or enthusiast can claim, show, or prove any different.

I still haven’t heard where the additional energy in the ocean heat index is coming from. The sun has not shone sufficiently brighter, there has not been sufficient change in cloud cover or change in ozone levels.

That the ocean dwarfs the atmosphere for energy content is a known. That makes it all the more fruitless to attempt to draw global lessons from a narrow examination of air temperatures.

Abe3 says:
The sun has not shone sufficiently brighter, there has not been sufficient change in cloud cover or change in ozone levels.

Henry says:
I’m not sure how you figured that out….. My (surface) data shows that more heat is going into the SH oceans. And, apparently, so does your data from the oceans/ BTW where did you get that info?

There is interaction between the magnetic forces of the sun and those of earth that may affect the movement of clouds. If for example, .due to a lack of winds in the Pacific, clouds are more inclined to move towards the poles (I checked: rain water contains iron) then you have a natural type of warming. On the equator your W/m2 is 684 whereas on average it is 342 W/m2 becoming less towards the poles.

Ozone is only coming back now but it is not yet at the same levels of 30 years ago. What many people do not realize is that a little less UV from the sun makes less ozone which reduces the shield that we have on top of us. So if the sun makes a little less UV we have less ozone which allows more sunlight in. Confusing , is it not? On the other hand, chlorine destroys ozone – and chlorine is coming from a lot of processes including the (now banned) CFC’s.

I was under the impression that 20th century heating in the Arctic was on the order of several degrees (3C above 1979-2000?). I do not think one needs to be concerned with figures well to the right of the decimal to find significance in Arctic heating. I was also under the impression that Arctic temperatures were currently higher than they had been in approximately 2,000 years.

As I said, I’m no climate scientist. As such, I do not understand what you intende to convey with your comment concerning Arctic surface temp’s correlation with nearby oceanic conditions.

The global rate is 0.014 per annum but it is next to nothing in the SH and in the antarctic.
I would not lose any sleep about it if I were you.
Don’t worry about driving your cars. It is OK. It is not the carbon dixide.

Climate change in the Arctic and Effects of global warming
(Wikipedia)

There are several reasons to expect that climate changes, from whatever cause, may be enhanced in the Arctic, relative to the mid-latitudes and tropics. First, is the ice-albedo feedback, whereby an initial warming causes snow and ice to melt, exposing darker surfaces that absorb more sunlight, leading to more warming. Second, because colder air holds less water vapour than warmer air, in the Arctic, a greater fraction of any increase in radiation absorbed by the surface goes directly into warming the atmosphere, whereas in the tropics, a greater fraction goes into evaporation. Third, because the Arctic temperature structure inhibits vertical air motions, the depth of the atmospheric layer that has to warm in order to cause warming of near-surface air is much shallower in the Arctic than in the tropics. Fourth, a reduction in sea-ice extent will lead to more energy being transferred from the warm ocean to the atmosphere, enhancing the warming. Finally, changes in atmospheric and oceanic circulation patterns caused by a global temperature change may cause more heat to be transferred to the Arctic, enhancing Arctic warming (ACIA 2004).

According to the Intergovernmental Panel on Climate Change (IPCC), “warming of the climate system is unequivocal”, and the global-mean temperature has increased by 0.6 to 0.9 °C (1.1 to 1.6 °F) over the last century. This report also states that “most of the observed increase in global average temperatures since the mid-20th century is very likely [greater than 90% chance] due to the observed increase in anthropogenic greenhouse gas concentrations.” The IPCC also indicate that, over the last 100 years, the annually averaged temperature in the Arctic has increased by almost twice as much as the global mean temperature has. In 2009, NASA reported that 45 percent or more of the observed warming in the Arctic since 1976 was likely a result of changes in tiny airborne particles called aerosols.

There was a period from the late 1920s to the early 1950s during which the Arctic was almost as warm as it is today, though the spatial pattern of today’s warming differs from that of the earlier period. Sea ice extent has decreased by 5.25% to 8.25% since 1979, the beginning of the reliable satellite record, with a larger decrease in summer (12.5% to 24.5%) than in winter (IPCC 2007).

Climate models predict that the temperature increase in the Arctic over the next century will continue to be about twice the global average temperature increase. By the end of the 21st century, the annual average temperature in the Arctic is predicted to increase by 2.8 to 7.8 °C (5.0 to 14.0 °F), with more warming in winter (4.3 to 11.4 °C; 7.7 to 20.5 °F) than in summer (IPCC 2007). Decreases in sea-ice extent and thickness are expected to continue over the next century, with some models predicting the Arctic Ocean will be free of sea ice in late summer by the mid to late part of the century (IPCC 2007).

A study published in the journal Science in September 2009 determined that temperatures in the Arctic are higher presently than they have been at any time in the previous 2,000 years.[2] Samples from ice cores, tree rings and lake sediments from 23 sites were used by the team, led by Darrell Kaufman of Northern Arizona University, to provide snapshots of the changing climate.[3] Geologists were able to track the summer Arctic temperatures as far back as the time of the Romans by studying natural signals in the landscape.[4] The results highlighted that for around 1,900 years temperatures steadily dropped, caused by precession of earth’s orbit that caused the planet to be slightly farther away from the sun during summer in the Northern Hemisphere.[2][3] These orbital changes led to a cold period known as the little ice age during the 17th, 18th and 19th centuries.[2][3] However, during the last 100 years temperatures have been rising, despite the fact that the continued changes in earth’s orbit would have driven further cooling.[2][3][5] The largest rises have occurred since 1950, with four of the five warmest decades in the last 2,000 years occurring between 1950 and 2000.[2] The last decade was the warmest in the record.[6]

I checked your “hanky panky” link and I find that your evidence that “results reported by UK and USA may have been compromised” consists of lower temperature readings from Gibraltar than from Tangiers, Granada and Malacca. I invite you to consult a map that shows both land and sea.

2)see: http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
My latest tables clearly imply that the observed warming over past 4 decades was largely due to natural causes. Either the sun shone a bit brighter or there were less clouds. Or there was less ozone , which could have had a human influence but not anymore (?) , shielding us. There are various theories.
Looking at the differences between the results from the northern hemisphere(NH) and the southern hemisphere (SH), what we see is happening from my dataset is that more (solar) heat went into the SH oceans and is taken away by water currents and/or weather systems to the NH. That is why the NH is warming and that is why the SH does not warm.

There certainly are various theories. Might I point out that precisely as much water moves north across the equator as moves south across the equator. The temperature differences in the air is due to the differing amounts of landmass in the two hemispheres.

3) A lot of the extra heat coming into the SH oceans condenses in the arctic, because of the cold there, releasing energy. (when water condenses it releases heat)

The Arctic is not in the Southern Hemisphere. Antarctica has not experienced anything like the warming that has taken place in the Arctic. The simple reason for the difference is that one pole is land surrounded by sea while the other pole is sea surrounded by land.

You make some good points but, with respect, they are not relevant to the subject of this thread.

It seems you are pressing them because you have misunderstood Spencer’s findings;
i.e. at April 1, 2012 at 7:51 am you say;

“I also wonder, if the global warming trend is simply the product of increasing urbanization:
1) Why did Peterson 2003 find so little UHI effect in general and Jones 2008 found no signal at all from increasing urbanization in China?
2) What is causing the increase in ocean heat content?
3) What is causing the dramatic air temperature increase in the Arctic?”

But Spencer did NOT find “the global warming trend is simply the product of increasing urbanization”.

He found
(a) that the UHI contributes to the calculated warming trend
and
(b) the degree of that contribution relates to population density in the calculated grid.

And the answers to your questions are interesting.

A to Q1.
Peterson and Jones used the data fabricated by Wang. So Peterson and Jones may (or may not) have conducted good analyses, but they analysed fraudulent data and, therefore, their results are meaningless.

A to Q2.
The ocean heat content increase is probably caused by the same mechanism (whatever that is) which caused the part of the warming trend which is not UHI.

A to Q3.
There is no reason to think warming of the polar regions is “dramatic”. In recent decades there has been negligible warming (probably slight cooling) of the Antarctic. Warming has happened in the Arctic but we have only been able to measure it in recent decades and there are good reasons to think the Arctic region should have higher temperature variability than elsewhere.

Hence, your questions are interesting but they are not relevant to consideration of Spencer’s findings.

People are asking “Why….Jones 2008 found no signal at all from increasing urbanization in China?
Have you read this paper co-authored by Jones;
History made as Jones et al 2008 paper admits huge urban warming in IPCC flagship CRUT3 gridded data over Chinahttp://www.warwickhughes.com/blog/?p=204
March 16th, 2009 by Warwick Hughes
Now Jones et al 2008 are saying in their Abstract, “Urban-related warming over China is shown to be about 0.1 degree per decade, hey that equates to a degree per century. Huge.
I think it is likely that Jones has had to be “dragged kicking and screaming” by his co-authors to go along with this conclusion.

Parker’s wind studies did not rely on Wang’s data. And, to be fair, UEA gave the following response:

Statement from the University of East Anglia in response to ‘UK scientist hid climate data flaws’ (Guardian, 02.02.10)
Tue, 2 Feb 2010

The allegations made in today’s Guardian create a misleading picture and require important clarifications in three areas:

1. The FOI request was responded to in full

The FOI request from Douglas Keenan was responded to by the university in full in 2007. The data used in the 1990 paper were indeed sent to Mr Keenan, including both the locations of the stations and the station temperature data for China, Australia and western parts of the former Soviet Union. For China, the data covered the period 1954 to 1983. The data were also uploaded onto the Climatic Research Unit (CRU) website.

2. The accuracy of the data and results was confirmed in a later paper

Prof Jones embarked on a study in 2007 which was published in the Journal of Geophysical Research in 2008. In this later study, CRU researchers worked with a Chinese colleague (Dr Q. Li) from the China Meteorological Administration (CMA) in Beijing. Dr Li had been assessing the consistency of 728 Chinese temperature series and his work was published in China in 2007. This improved CMA data was adjusted to account for changes in location of stations.

CRU requested this improved CMA data for the stations that had been used in the 1990 study, and they were incorporated into the 2008 paper.

Figure 6 from this study (see below [not shown]) shows the comparisons (as anomalies from the 1954-1983 period) between the averages of the 42 rural and 42 urban sites used in 1990 compared with averages from the same stations from the CMA network. The dashed lines are the averages for the rural and urban sites in eastern China from the 1990 paper. The solid lines are the averages from the same stations from the CMA network. It is clear from the graph that the trends of the CMA data for both the rural and urban networks agree almost exactly with the results from the 1990 paper.

The 2008 study undertook additional analyses using more extensive data and did conclude that there was a likely urbanization trend in China of 0.1 degrees Celsius per decade for the period 1951-2004. But allowing for this, there was still a large-scale climatic warming of 0.15 degrees C per decade over the period 1951-2004 and 0.47 degrees C per decade over the period 1981-2004. The paper concluded that much of the urbanization trend was likely due to the rapid economic development in China since the 1980s, after the period analysed in the 1990 paper.

3. The CRU findings were corroborated by other papers used by the IPCC

The 1990 paper was only one of a number of papers referred to in the 2007 IPCC Report examining possible urbanizations effects.

Abe 3 says:
….Gibraltar than from Tangiers, Granada and Malacca. I invite you to consult a map that shows both land and sea.

Henry@Abe3

It is not Malacca. It is Malaga. I suggest you get a map from Spain and you will see that Gibraltar lies in the middle of Tangiers, Granada and Malaga.It is the difference in the rise of maxima at Gibraltar that is (very) strange is it not?

Your post at April 1, 2012 at 7:35 pm suggests you are attempting to disrupt this thread. It follows my post at April 1, 2012 at 3:18 pm that explained;
“You make some good points but, with respect, they are not relevant to the subject of this thread.

But you have responded with a long screed which purports to support your irrelevance.

The post by wshofact at April 1, 2012 at 4:31 pm proves the Jones paper you cited is wrong. Your subsequent long screed drops that and concentrates on trying to justify the claim by Parker that wind speeds indicate temperature better than thermometers.

This debate of irrelevance is inhibiting discussion of the subject of this thread. I suspect I am not alone in wanting you to stop it.

Climate change in the Arctic and Effects of global warming
(Wikipedia)
…

Wow, a mass copy and paste of a Wikipedia entry, about climate, including references.

1. Wikipedia’s bias about climate issues is well known around here, so it will be ignored.
2. You didn’t indicate what were the relevant bits that should be noted, nor indicated why you thought it was important to copy it here, so it will be ignored.
3. There is no “Climate change in the Arctic and Effects of global warming” entry currently in Wikipedia. The cited references are getting old. There is a current “Climate change in the Arctic” entry that looks completely different:http://en.wikipedia.org/wiki/Climate_change_in_the_Arctic
Thus what you posted can be ignored.
4. As regular WUWT readers will know, the Arctic climate system is an area of active research where new discoveries are being made, new hypotheses formed and tested with new theories established. We know we don’t know everything yet, there are still things to learn. There have been interesting revelations since January 2010, the latest reference date, with some things disproven. Thus what you posted can be ignored as dated.

So all the references for all that you copied and pasted are:
1. NASA saying 2000-2009 was warmest decade of a record only going back to 1880, which is hardly controversial.
2. Debunked paper with unknown correction which can be inferred as an admission it had at least one flaw so egregious it had to be formally noted.

Abraham3 says:
(I) have no qualifications to perform an informed debate

Henry says:
I thought you just wanted to know where the extra heat in the oceans came from. I told you: my data, 1.5 million of them, suggest it is from more sunshine and/or less clouds and/or less ozone. A lot of the extra heat goes in the SH oceans. In the SH there is little evidence of increasing temperatures presumeably because there is much less landmass than on the NH and/or it does not get the warmer currents on the right places, e.g. Durban in SA actually became colder. \My data also suggest that the (extra) heat then moves northwards, by currents and weather systems, where it is used to melt some arctic ice and it is also trapped by increasing vegetation. Maybe there are more factors. If you go to Norway , to the arctic, you will be amazed about all the waterfalls, and condensation that goes on there. It is teeming with life. Everywhere. The water is in the air. That is why more warming is good for you. The Norwegians are actually happy with the arctic becoming more habitable because now they can get more oil and gas.
End of debate.

Another, obvious thing, something I haven’t seen mentioned here, though someone must have brought it up: That cities contains tens of thousands of windblocks called “buildings”. Wouldn’t blocking this lower wind slow down the thermostatic cooling effect that Willis E. describes so well, besides obviously giving the air more time to heat up?

This occurs in the country as well, only the blocks there are called “trees”. Of course trees respire and cool (but raise local humidity) where buildings don’t and warm (but are dry). The problem is once again not simple, linear, or (probably) linearizable. Not only are trees going to be different from farmland, farm land different from desert, hills different from plains, cities different from towns, lakes and rivers different from dry (but not desert) land in all types of use, oceans and coastlines different from everything, but different kinds of trees, or crops, or deserts, are likely to be different in different places.

In another post I mentioned the variability I can observe in recorded temperatures just moving the recording thermometer to different places on my 1/2 acre plot of property in a semirural setting. Front of the house different from the rear different from under the trees different from over the driveway different from anywhere near the roof different under the deciduous trees in front with relatively high vaulted canopy and the tightly packed cypresses in the rear.

I’m curious about whether or not this has been properly studied. I’m very tempted to buy one of the really good weather stations for the sole purpose of moving it around to different locations on my property every night (randomly selecting a new location by rolling a die, as it were) and recording the thermal trace at six different places as six different randomly scattered threads, to see how distinct the max-min-mean-variance sort of behavior is, averaged out seasonally by the random daily site selection.

If I were rich and bored, I’d probably do the same thing with sites in Duke forest, on nearby farmland, in the city, and so on, moving the same unit to all of these places so the traces were randomly selected from the same thermometers, not systematic but from different thermometers, all within a (say) three mile radius of my house. Within three miles I can’t quite reach the main part of downtown but I can hit malls with major high-rise offices, stream beds, Duke itself, farmland, and major rural forest with huge populations of deer and differing kinds of trees, at heights above sea level that vary by at least 50-100 meters (I live on the slope of a ridgeline that stretches from Chapel Hill to Hillsborough, with Durham proper the slightly flatter land off to the side). I’d bet that mean temperatures at the sites vary by at least 2K, maybe even 3K, from the hottest to the coldest (all measurements with the same thermometer).

In case the point of my previous article wasn’t clear — it is stupid to try to infer the UHI effect from antique data, when one can just bloody well measure it directly and on purpose with a suitably designed experiment now, and to very high precision indeed. Anthony already took the first steps towards this when he documented the poor siting of many weather stations — the obvious and directly measurable conclusion is that those poorly sited stations almost invariably show more warming although all one has to do is look at patterns of snow-melt after a fall to realize that it is possible (but less likely) to site a station that stays significantly colder than “true ambient temperature” (whatever that means) as well.

This isn’t to criticize Roy’s efforts to do so (any more than I would criticize Jones et all, or any of the many others that have tried to do so) — it is just that this is a great big why do so when it is so easy to design an actual experiment to directly measure it and in the process contruct a fine-grained picture of spatial thermal variation as multivariate function that might actually permit inferences to made from the historical data the other way — if nothing else accurately characterizing the noise and bias associated with our ignorance of siting and the measurement process used for ancient historical thermometry. If there are degrees K of noise to be had in a 1/2 acre plot, and more degrees K of systematic noise to be had in a three mile circle, one has to deconvolve a complicated multivariate systematic bias function in order to statistically “correct” otherwise anonymous temperature readings (and those readings will end up with large error bars).

Why infer when you can measure? The same is true for the GHE — why infer it, model it, assume it, when the open desert provides one with ample opportunity to directly measure it in air with minimal confounding moisture?

when Roy and myself and others are estimating global warming at ca. 0.014 degrees C per annum over the paswt 4 decades we do so because we looked at thousands of data and we hope that the average will give us a good estimate of what is going on. At the same time I realize that accuracy may have improved in the last 40 odd years, both in measurement as well as the exclusion of human influence. Probably more so twoards the higher measurement. Your arguments abouyt location also hold true. I have noticed that will actually find cooling where there they cut trees:http://letterdash.com/HenryP/de-forestation-causes-cooling

Gidday rgbatduke and other commenters – you mention wind, buildings and UHI –
Do you know the book, “The Urban Climate” – Helmut E.Landsberg – a solid source of information.
Your library might have it or be able to get it.
For $30 you can have a copy mailed to you – thru abebooks.com

Abraham3:
Your post at April 1, 2012 at 7:35 pm suggests you are attempting to disrupt this thread.

I say:
You are incorrect. I am attempting to discuss Spencer’s hypothesis. Reading through the comments on this web site, one is struct by the tremendous amount of support Spencer receives here and the extraordinarily small amount of criticism. I was hoping, in my own small way, to move things toward a more balanced and equitable configuration.

richardscourtney says:
It follows my post at April 1, 2012 at 3:18 pm that explained;
“You make some good points but, with respect, they are not relevant to the subject of this thread.

I say:
I have to disagree.

richardscourtney says:
But you have responded with a long screed which purports to support your irrelevance.

I guess I failed to realize that your comment was an order to stop or that you possessed the authority to give out such commands in the first place.

I advised in my first post here that I was not a climate scientist. Given that, I had no idea that an entry from Wikipedia would be greeted with such disdain. I’m sure it lacks some of the numerical details you folks love to bandy about, but I’m also quite sure that it is a great deal more objective than most of the references I’ve seen used on this site.

richardscourtney says:
The post by wshofact at April 1, 2012 at 4:31 pm proves the Jones paper you cited is wrong.

I say:
It most certainly does not. She references a blog singing gleefully that Jones found 0.1C/decade urban warming in developing China – signficantly more warming then he found in 1990. But since I cited Jones 2008, the error here is yours.

richardscourtney says:
Your subsequent long screed drops that and concentrates on trying to justify the claim by Parker that wind speeds indicate temperature better than thermometers.

I say:
Since I made no error citing Jones 2008, there was nothing to drop. No one here or elsewhere has challenged it. Parker’s methodology for looking for diferentiating urban heat sources is valid and if you want to make comments about it, you might want to actually familiarize yourself with it first as it made no attempt whatsoever to measure temperature via wind speed.

richardscourtney says:
This debate of irrelevance is inhibiting discussion of the subject of this thread. I suspect I am not alone in wanting you to stop it.

I say:
Take a quick scan through the 178 comments on this article and tell me what proportion support Spencer’s results and what proportion questions them – then explain to all of us how you can call what you have going on here a discussion, much less a debate. It’s a mutual back-slapping sing-along.
********************************************************************************************************
As I pointed out to the inestimable HenryP, a high reading from a UHI is not going to influence the gridded reading from an urban station hundreds of miles away. The distance over which interpolation is conducted is limited. Therefore, by selecting only cells in which all three population densities exist – where all are in close proximity, Spencer has guaranteed that he has found the relatively rare areas in which they do.

Having a thousand thermometers does not give a cell more weight in the process of surface temperature determination. That there are more stations in populated area does not cause a bias – at least in any study by middle school grads and above. GISS, NASA, NCDC/NOAA and Hadley have all gone to great lengths to correct UHI effects where necessary. The presence of UHI’s has not corrupted, much less falsified the global warming seen in the last 150 years. UHIs have had no detectible effect on sea temperature and given the 272-fold edge the ocean has over the atmosphere in mass, I still have confidence that the data the rest of the world’s climate scientists are providing is trustworthy – at least more trustworthy than anything I’ve seen out of Dr Spencer.

An economist friend with no dog in this fight and who has not followed the whole CAGW debate or Climategate closely (a director in a large government “three letter” research institute) had these observations (comments / references / rebuttals welcome):
—
Interesting. No physics, so I have a fighting chance of understanding it. The theoretical argument is unassailable so it is just a simple hypothesis test. Potential selection bias and possibly small sample issues make me suspicious. The lament that peer review publication is impossible sounds completely bogus. Also, the wringing of hands at the size of the dataset is highly correlated with chicanery. I would be extremely surprised if there is not a rich peer-reviewed literature on the urban heat island hypothesis. I would also be extremely surprised if one could get an article published using these data to measure average temps without somehow controlling for the effects of urban heat sinks on the average results.
—

I advised in my first post here that I was not a climate scientist. Given that, I had no idea that an entry from Wikipedia would be greeted with such disdain. I’m sure it lacks some of the numerical details you folks love to bandy about, but I’m also quite sure that it is a great deal more objective than most of the references I’ve seen used on this site.

Your apparent wholesale quoting of a Wikipedia entry on Arctic climate objectively boiled down to one NASA statement on global temperature and a single debunked questionable paper on the Arctic. It also is not currently in Wikipedia, doesn’t match current Wikipedia contents, and I have no evidence it ever was a real Wikipedia entry. Even by Wikipedia standards, it should have had more references than those two and four articles regurgitating and interpreting the paper’s press release.

If that’s enough for you to evaluate it as more objective than the usual plethora of sources found on WUWT, then you have far greater problems judging references and sources than any that could possibly arise from not being a climate scientist.

Spencer: You can test for error due to a bias in the spacial sampling by randomizing the datasets against the positions of the stations involved. Just assign to each temperature set a randomly chosen location. Your population class plot should come out with all bins at the same hight. Do this several times to get an idea of the intrinsic variance due to the spacial distribution.

Why don’t you write a formal paper and send it to the arxiv.org e-print server as a statistical paper? That would bypass the Inquisition.

People like Roy and me are most probably not interested in writing papers. We only want to figure out the truth from what has been observed and measured, irrespective of the errors and inaccuracies involved….
It is pure “self interest”. If other people want to listen, it is up to them. People like Abraham 3 can stay in their ignorance as far as I am concerned.
But seeing you are probably from the low lands, I am going to show you an interesting puzzle.

Note the following results from New York Kennedy Airport (lat. 40.65)
Maxima rising at 0.178 degrees C per decade since 1973
Means rising at 0.152 degrees C per decade since 1973
Minima going down at -0.011 degrees C per decade since 1973

We note that in LV the average temps. are pushed up mainly by increasing minima.
But not so in NY, because minima are not rising there.
Seeing as to both are (increasing) urban areas, any ideas as to why there is a difference?

You are either a liar or very wrong. Pick one, those are your only options. You provided the address, same one I did. Have you even bothered to look at that entry?

Your “quote”, first line:
“There are several reasons to expect that climate changes, from whatever cause, may be enhanced in the Arctic, relative to the mid-latitudes and tropics.”

The Wikipedia Climate change in the Arctic entry, which I had looked at before, doesn’t have that line, and it doesn’t even have the word “reason” which I searched for just now.

But, Google did find that line, in the Wikipedia Climate of the Arctic entry, specifically the Global Warming section. By current Wikipedia standards this entry is neglected. What showed up in your “quote” as a list of references is identified as “Notes” at the bottom, with those references limited to the “Global warming” section. Below “Notes” is “Bibliography” which gives references used elsewhere without links in the text to them, also that other text uses many external links which go against current Wikipedia recommendations:http://en.wikipedia.org/wiki/Wikipedia:External_links
“Wikipedia articles may include links to web pages outside Wikipedia (external links), but they should not normally be used in the body of an article.”

This indicates the entry is not up to current Wikipedia standards, and the “Global warming” section was added later than the rest. As seen in the real true “Climate change in the Arctic” entry, those many assorted references of different types should be assembled into a “References” section with appropriate linkages.

And the “Global warming” section you actually did copy, still has its references objectively boil down to a single NASA statement on global temperature and a single debunked questionable paper.

So which is it? Are you an outright liar, or merely very wrong because you’ve never bothered to verify the source you have adamantly insisted is the true origin of your “quote”?

You guys need to see a little more mainstream science in here.

Second line of the section you did quote:
“First, is the ice-albedo feedback, whereby an initial warming causes snow and ice to melt, exposing darker surfaces that absorb more sunlight, leading to more warming.”

…No tipping point for Arctic Ocean ice, study says
…
(…) Our results suggest that anomalous loss of Arctic sea ice during a single summer is reversible, as the ice–albedo feedback is alleviated by large-scale recovery mechanisms. Hence, hysteretic threshold behavior (or a “tipping point”) is unlikely to occur during the decline of Arctic summer sea-ice cover in the 21st century.
…

…
In a presentation at the 242nd National Meeting & Exposition of the American Chemical Society (ACS), Mark Z. Jacobson, Ph.D., cited concerns that continued melting of sea ice above the Arctic Circle will be a tipping point for the Earth’s climate, a point of no return. That’s because the ice, which reflects sunlight and heat back into space, would give way to darker water that absorbs heat and exacerbates warming. And there is no known way to make the sea refreeze in the short term.

Jacobson’s calculations indicate that controlling soot could reduce warming above parts of the Arctic Circle by almost 3 degrees Fahrenheit within 15 years. That would virtually erase all of the warming that has occurred in the Arctic during the last 100 years.
…

Mainstream science, especially work more current than what’s in your “quote”? We got that.

You are correct, I was wrong. My quote did indeed come from Wikipedia’s “Climate of the Arctic” and not “Climate Change in the Arctic”. My apologies. I allowed myself to be fooled by the presence of the same graphic at the beginning of the section I actually quoted and at the beginning of the article I erroneously identified as my source – and, of course, the similarity of the two titles..

I would ask, however, that you withdraw your use of the word “liar”. Such a charge was uncalled for and is unproductive. There is no rational reason why I would have lied about such a thing and the fact that you located the actual source of my quotation – in Wikipedia and with the references cited – leads me to wonder what it was that actually motivated your charge.

Re Tietsche et al: the study looks at recovery from loss of ice over a single summer. If the process they propose – increased radiation from open water vice ice – were dominant, Arctic sea ice would not be in decline. Their study demonstrates a mollifying mechanism assuring the unlikelihood of a “tipping point” leading to runaway melt. It does not refute the loss of albedo from melting ice nor the continued melting from increased temperatures.

It is good to hear from Dr Jacobsen that controlling soot might save Arctic sea ice but I see nothing in his work questioning the ongoing melting, the cause of that melting or the self-reinforcing nature of albedo loss.

The Arctic is melting and the cause has nothing to do with urban heat islands.

Well I looked at some temp. data as reported from the Elmendorf Airforce base in Anchorage.
If we look at the data from 1973 I find:
Maxima increasing at 0.422 degrees C per decade
Means (average temps) rising at 0.162 degrees C per decade
Minima decreasing -0.026 degrees C per decade

Interestingly enough, we see that UHI is absolutely not a factor here. The increasing average temp. at the base was caused by natural warming.
As I have tried to explain to you before,
if it were us heating the place, (e.g. burning stuff, UHI, etc) or if it were earth itself heating the place up (e.g.more underground volcanic activity, or more vegetation) or if there were some increased greenhouse effect, we would have found the exact opposite trend, namely minima pushing up the average temperature.
Even if snow removal were a factor, we should be seeing increasing minima. What we see from the data from the airforce base is exactly the opposite. It is the increasing temperature that happens during the day that is pushing up the temperature at the airforce base in Anchorage.
As I said to you, again and again, globally it is also like that: global warming is caused by the increasing sunshine and/or less clouds and or less ozone shielding. Now let us celebrate warming and let us hope that it lasts! (icy weather is not so good for the promotion of life)

I’m terribly sorry Henry, but you are completely incorrect. The current solar irradiance trend is downward and has not matched global temperature curves since records were first kept.

The dominant factor warming the Earth is infrared trapped by increasing levels of CO2 in our atmosphere and virtually every molecule of CO2 in the atmosphere above the 280 ppm, pre-industrial level is anthropogenic.

Abraham 3 says:
The dominant factor warming the Earth is infrared trapped by increasing levels of CO2 in our atmosphere and virtually every molecule of CO2 in the atmosphere above the 280 ppm, pre-industrial level is anthropogenic

Henry says:
Surely you don’t actually believe that ? I could find no such proof from the temp. data.
I have now analysed all the daily results coming from 19 weatherstations in the SH
and found the following averages:

I am busy cutting up my tables and doing regression (trends) from the beginning (last ca. 37 yrs), then 32 yrs, then 22 years, then 12 years, then the last 7 years. The intervals are more or less randomly chosen. Unless sombody has other (better) ideas about the intervals?

I’ve been wondering for some time if air conditioning has something to do with this (I
haven’t read all 191 replies).
Almost all buildings in the U.S. have air
conditioning and when the air inside is cooled the heat is transferred outside via the A/C system.